HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents
Authors: Tristan Tomilin
Meng Fang
Mykola Pechenizkiy
Venue: ICLR 2025 Poster (2025)
Keywords: reinforcement learning
AI safety
safe RL
constrained RL
benchmark
vizdoom
3D
difficulty levels
Primary area: datasets and benchmarks
TL;DR: A Safe RL benchmark for vision-based learning in complex navigable 3D environments.

Abstract: Advancing safe autonomous systems through reinforcement learning (RL) requires robust benchmarks to evaluate performance, analyze methods, and assess agent competencies. Humans primarily rely on embodied visual perception to safely navigate and interact with their surroundings, making it a valuable capability for RL agents. However, existing vision-based 3D benchmarks only consider simple navigation tasks. To address this shortcoming, we introduce **HASARD**, a suite of diverse and complex tasks to **HA**rness **SA**fe **R**L with **D**oom, requiring strategic decision-making, comprehending spatial relationships, and predicting the short-term future. HASARD features three difficulty levels and two action spaces. An empirical evaluation of popular baseline methods demonstrates the benchmark's complexity, unique challenges, and reward-cost trade-offs. Visualizing agent navigation during training with top-down heatmaps provides insight into a method's learning process. Incrementally training across difficulty levels offers an implicit learning curriculum. HASARD is the first safe RL benchmark to exclusively target egocentric vision-based learning, offering a cost-effective and insightful way to explore the potential and boundaries of current and future safe RL methods. The environments and baseline implementations are open-sourced.

Published as a conference paper at ICLR 2025
HASARD: A BENCHMARK FOR VISION-BASED SAFE REINFORCEMENT LEARNING IN EMBODIED AGENTS
Tristan Tomilin'
Meng Fang2! Mykola Pechenizkiy'
Eindhoven University of Technology Universityy of Liverpool (t. wt..immiiinniii m. pechenizkkyy @t Meng. Fangel www.:lloleepoo.....m ac.uk
ABSTRACT
Advancing safe autonomous systems through reinforcement learning (RL) requires robust benchmarks to evaluate performance, analyze methods, and assess agent competencies. Humans primarily rely on embodied visual perception to safely navigate and interact with their surroundings, making a valuable capability for agents. However, existing vision-based 3D benchmarks only consider simple navigation tasks. To address this shortcoming, we introduce HASARD, a suite diverse and complex tasks to HArness SAfe RL with Doom, requiring strategic decision- decision-making. comprehending spatial relationships, and predicting the short- term future. HASARD features three difficulty levels and two action spaces. An empirical evaluation of popular baseline methods demonstrates the benchmark's ss complexity, unique challenges, and reward-cost trade-offs. Visualizing agent navigation during training with top-down heatmaps provides insight into method's s learning process. Incrementally training across difficulty levels offers an implicit learning curriculum. HASARD is the first safe RL benchmark to exclusively target egocentric vision-based learning, offering cost-effective and insightful way explore the potential and boundaries of current and future safe RL methods. The environments and baseline implementations are open-sourced
Armament Burden
Volcanic Venture
Detonator's s Dilemmaa
200 1OOM
Remedy
Collateral Damage
Precipice Plunge
Rush
BOON
TOooz
Figure 1: HASARD enviromments offer rich diversity in visuals, objectives, and features. Each setting poses unique safe RL challenges across dynamic 3D landscapes, requiring memory, strategic naviga- tion, tactical decision-making, responsiveness to sudden changes, and estimating future states. Higher difficulty levels introduce novel features beyond basic parameter adjustments. While lacking visual fidelity and accurate physics, HASARD effectively mimics real-world navigation and interaction.
Visit project wehsite httphhh/tt///eeeeeeeeeeee http:://///ete.g.ggglee/

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INTRODUCTION
Over recent decades, reinforcement learning (RL) has evolved from a theoretical concept into transformative technology that impacts numerous fields, including transportation scheduling (Kayhan & Yildiz, 2023), , traffic signal control ( (Liagg et al. 2019), energy management (Wei et al.) 2017). and autonomous systems (Campbell et al. 2010). Its application ranges from optimizing complex chemical processes (Quah et al. 2020) to revolutionizing the entertainment (Wang et al. 2017) and gaming |Vinyals et al. 2017; Berner et al. 2019) industries. However, as RL continues to integrate into more fiffety-rriticll applications such as autonomous driving, robotics, and healthcare, ensuring the safety of these systems becomes paramount. This need arises because failures in real-world RL applications can lead consequences ranging from minor inconveniences to severe catastrophes.
Despite the importance of safe RI, the development of its systems faces many hurdles, including the lack of robust benchmarks that mimic real-world complexities while remaining computationally efficient for rapid simulation and training. Researchers have often resorted to manually adapting
existing RL environments (Alshiekh et al., 2018: Gronauer et al. 2024) and simulation platforms (Müleer et al., 2018; Lesage & Alexander 1021 to test safety features. This complicates repro- ducibility and often lacks the documentation and baseline comparisons necessary for streamlined scientific progress. Existing safe RI. benchmarks often rely on simplistic 2D toy problems to test rudimentary capabilities (Leike et al. 2017 Chevalier-Boisvert et al.] (024). or focus on learning safe robotic manipulation and control from proprioceptive data (Ray et al. 2019 Duloc-Armold et al. 2020 Yuan et al., 2022 Gu ct al. 2023). However, few simulation environments have been developed that support embodied egocentric learning from imagery, a critical component for applications where visual perception directly impacts decision making and safety. It allows agents to interpret and interact with the environment from first-person perspective, which is essential for complex scenarios such as autonomous driving, assistive robotics, unmanned aerial vehicle navigation, and industrial automation. Focusing on embooiied-perceplee learning not only improves agent's performance in dynamic, visually diverse settings but also brings its processing closer to human perceptual and cognitive processes.
To address these gaps, we introduce HASARD, a benchmark tailored for egocentric pixel- safe RL. It features a suite of diverse, stochastic environments in a complex 3D setting. HASARD demands comprehensive safety strategies and higher-order reasoning that go well beyond mere navigation tasks in prior simulators WWymann et al. (2000); Dosovitskiy et al. (2017) Li al. (2022); Ji et al. (023a)) Existing benchmarks often rely on physics-based simulation engines, such as MuJoCo (Todorov et al. 2012) or Robosuite (Zhu et al. 2020), which are computationally expensive and slow. In contrast, HASARD is built the ViZDoom (Kempka al. (0166) platform and is integrated with Sample-Factors (Petrenko et al. 2020), enabling up to 50,000 environment iterations per second on standard hardware.
Instead of focusing on learning safe control under complex physics simulations, our setting features semi-reale realirrelistic environments that effectively replicate critical aspects of real-world interaction such as spatial navigation, depth perception, tactical positioning, target identification, and predictive tracking. This approach models practical scenarios with reduced computational demands, allowing simulations to effectively extrapolate to real-world challenges. Incorporating vision into safe RL is crucial for enhancing realism and applicability by mirroring human perception in diverse, safety-critice tasks. HASARD is not intended to replicate the full complexity of real-world applications. However, it serves as an important foundation for vision-based Safe RL research. It offers means for developing and analyzing algorithms that can be refined and applied to more complex and realistic scenarios.
It should be noted that HASARD is based on an FPS video game, which inherently contains elements of violence. We do not endorse these violent aspects in any way. Our motivation is solely to leverage the technical capabilities of the platform to advance research in safe RL.
The contributions of our work are three-fold. (1) We develop six novel ViZDoom environments
in three difficulty levels, simulating complex and visually rich 3D scenarios for safe RL. (2) By integrating these environments with Sample-Factory, we enable rapid simulation and training, and we publicly release HASARD, the first safe RL benchmark specifically designed for iusion-based embodied RL in complex 3D landscapes. (3) We evaluate six popular baseline methods in various settings within our environments, revealing key shortcomings in balancing task performance with safety constraints and guiding future research in safe RI.
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Table Comparison of existing Safe Reinforcement Learning benchmarks with HASARD.
Difficulty Levels
Vision Input
Light- weight
Action Space
Partially Observed
Benchmark
3D
Stochastic
AI Safety Gridworlds
Discrete
Lafe-Control-Gym
X
Continuous
Safe MAMuloCo
X
X
X
Continuous
MetaDrive
X
V
V
V
Disc./Cont.
Xi
CARLA
V
V
X
Continuous
Safety Gym
X
Continuous
Safety Gymnasium
X
X
Continuous
XI
HASARD
Disc./Hybrid
RELATED WORK
Adapted RL Environments RL simulation environments have widely been adapted for safety research. Specific tiles incorporated into Minigrid BhhiselierBBoisvertt et al. (024) environments serve as hazards that the agent must avoid (Wachi et all.[222]] Wang al. 2024). RWRL augments RL environments with constraint evaluation, including perturbations actions, observations, and physical quantities with robotic platforms such as two-wheeled robots and quadruped (Dulac-Arma al. 2020). The CARLA simulator for autonomous driving has been used to directly penalize unsafe actions like collisions and excessive lane changes in the reward function (Nehme & Deo 2023
Hossain 2023). Similarly, the racing simulator TORCS incorporates penalties for actions that lead to speed deviations and off-track movement into its reward structure (Wang et al. 2018).
Safety Environments Early safe RL environments like AI Safety Gridworlds (Leike et al. 2017) situated in 2D grid worlds and tackle challenges like safe interruptibility and robustness to distributional shifts. Robotics platforms directly incorporate safety constraints into RL training for tasks such stabilization, trajectory tracking, and robot navigation. Safe-Control-Ce 2022), effective for sim-to-real transfer, includes tasks like cartpole and quadrotor with dynamics disturbances. Mcanwhile, Safe MAMuJoCo, Safe MARobosuite, and Safe MAIG (Gu al. 2023) serve benchmarks for safe multi-agent learning in robotic manipulation. Safety- (Ray al. 2019) uses the pycolab engine for simple navigation tasks emphasizing safe exploration and collision avoidance. Sefety-Gymmasiune (Ji et al.) (023a) enhances Safety Gym with more tasks, agents, and multi-agent scenarios. The Safety Vision suite is the closest to our work, but despite the 3D capabilities of MuJoCo, these environments do not leverage the physics-based the engine to increase the depth and realism tasks but merely add a layer of control difficulty, accompanied by increased computational overhead. Furthermore, all tasks can fundamentally be reduced to two-dimensional problems as there is no vertical movement, limiting agents to navigation objectives where the goal is to avoid collisions while moving toward target. Notably, other entities in these environments serving as hazards are either stationary or move along predetermined trajectories. We present an extended comparison with Safety- Seffey-Gymmnsiimm in Appendix
(Yuan et al.)
nature of
3 PRELIMINARIES
In the context of embodied image-based safe reinforcement learning, we formulate the problem as a Constrained Partially Observable Markov Decision Process (CPOMDP), which can described by the tuple (S, O, AA, P. O, R, y, P.P,C.C d). In this model, S represents the set of states and O, the set of high-dimensional pixel observations, reflects the partial information the agent receives about the state. Actions are denoted by A, and the state transition probabilities by P = P(s | az). The observation function O(o]8141, a) dictates the likelihood of receiving an observation or E Ω after action a, and transitioning to state-action pairs to rewards. C is set cost functions c; : S A + R for each constraint i, while d is vector of safety thresholds. The initial state distribution is given by p. and the discount factor determines the importance of immediate versus future rewards. The goal in a CPOMDP is to maximize the expected cumulative discounted reward, E 7 (r)(st,a)), subject to the constraints that the expected cumulative discounted costs for each E (cc, remain below the thresholds d; A policy π + ((A) maps states to a probability distribution over actions. The value function / (s) and action-value function Q" (s, a) respectively measure the expected return
state St+1- The reward function R S A I, maps
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x
(a) Precipics P1 Plunge: The agent adopts safe strategy (left), carefully descending the cave using the staircase to minimize the risk of falling. The agent engages in unsafe leap to the bottom of the cave (right), quickly achieving its objective the cost of incurring significant fall damage.
(b) Det.onatoo'' 8 Di emma: The agent detonates barrel a safe manner (left), ensuring no creatures or other barrels are nearby while maintaining suffi- cient distance. The agent demonstrates unsafe behavior (right) by recklessly detonating a barrel in crowded area with nearby barrels while standing too closc.
Figure 2: Illustrations of safe and unsafe agent behavior.
from state under policy n, and after taking action in state Considering the numerous constraint formulations in literature (Gu et al., 2022 Wachi et al.) (024b). for the scope of this paper and our experiments, we adopt a CMDP approach where the aim to optimize the expected return from the initial state distribution p. constrained by safety threshold on the cumulative safety cost. Formally, the problem is defined as:
max V (p)
s.t. (z( S.
where V, and V.7 (p) respectively represent the expected return and the cumulative safety cost of policy n, and & E ER is the predefined safety threshold.
4 THE HASARD BENCHMARK
With the environment design inspired by previous benchmarks (ooillnn 2022 2024), HASARD
is built on the ViZ.ooo platform (Kempka et al.) 2016). highly Hexiblle RI. research tool that enables learning from raw visual inputs using the engine of the classic FPS video game, Doom. One of ViZ.Doom's 's key advantages is its lightweight nature, which allows it to achieve up to 7000 FPS with off-screen rendering. Table I compares HASARD with prior benchmarks. We motivate the utility of HASARD in Appendix
HASARD comprises six stochastic environments, developed using the Action Code Script (ACS) language. Table outlines the properties of each scenario, including (1) the simplified action space, (2) the presence of enemies, (3) the availability of obtainable items, (4) the primary objective of the environment, and (5) cost increasing criterion. Below, we will briefly introduce each scenario's challenges and characteristics.
4.1 SCENARIOS
A Armament Burden The agent must collect weapons scattered across the map and deliver them to the starting zone. Each acquired weapon adds to the load, slowing the agent once the carrying capacity is exceeded. Since heavier weapons yield higher rewards, the challenge is to optimize which weapons to collect, in what order, and when to return.
Remedy Rush The ask features an area densely filled with items that grant health or incur costs. The agent is required replenish its health by collecting the former and avoiding the latter.
Collateral Damage Armed with rocket launcher and held stationary. the agent is tasked to eliminate fast-moving distant targets while avoiding harm to neutral units in proximity. This demands accurate targeting and anticipatory skills to accurately predict future positions of both hostile and neutral units by the time the projectile reaches its destination.
Volcanic Venture
The agent must navigate platforms, skillfully leaping between them to collect items. This compels the agent to assess the feasibility reaching isolated platforms and their potential rewards, balancing the risk of falling into the lava.
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Table 2: Key aspects of HASARD environments including the simplified actions, presence of enemies, availability of collectibles, primary objective, and associated costs. Actions in the table follow the coding F: [OVE_FORWARD MOVE BACKWARD, L: TURN LEFT, T, TURN_RIGHT E: USE, J: M JUMP, S: SPEED, A: ATTACK, U: LOOK D: LOOK_DOWN.
Environment
Basic Actions
Enemies Items Objective
Cost
Armament Burden
FLREJ
Deliver weapons
Breach capacity
Remedy Rush
FLRJS
X
Collect health items
Obtain poison
Collateral Damage
ALR
Eliminate targets
Harm neutrals
Volcanic Venture
FLRJS
X
Gather items
Stand on lava
Precipice Plunge
FBLRUDJ
X
Descend deeper
Fall damage
Detonator's Dilemma
FLRJSA
X Detonate barrels
Damage entities
Precipice Plunge Trapped in a cave, the agent must quickly descend to the bottom. needs to skillfully control its movement and speed, accurately gauge depth, and assess which surfaces safe to leap to, as long falls result health loss.
Detonator's Dilemma The agent has to shoot explosive barrels scattered across the environment detonate them. The presence of neutral units sporadically roaming the area complicates the task. The agent must choose which barrels to shoot and time these detonations to prevent: 1) harming the neutrals, harming itself, and 3) unintended chain explosions.
All environments balance high rewards against low costs and are designed such that costs can always be avoided. As per Equation [ even when unsafe behavior is mitigated to keep costs within budget, refined strategies could yield higher rewards. This design ensures the benchmark remains challenging as safe RL. methods evolve. Further details of the environments can found in Appendix A
4.2 ENVIRONMENT SETUP
Each episode starts from a random configuration and runs for 2100 time steps (35 ticks/second for 60 seconds in real-time) unless terminated early. The environment's inherent stochasticity results in non-deterministic transition function P.
Observations The agent perceives the environment through first-person perspective, capturing each frame as a 160 x 120 pixel image in 8-bit RGB format. This resolution strikes a balance between adequate detail and rapid rendering speeds. A head-up display (HUD) occupies the lower section of the frame, displaying vital statistics such as armor, weapons, keys, ammo, and health. Limited by 90-degree horizontal field of view, the agent's observation is restricted to subset of its surroundings, defining the partially observable state space O of the CPOMDP
Actions Doom was originally designed for keyboard use, supporting a basic set of button presses for movement, shooting, opening doors, and switching weapons. Modern adaptations extend these capabilities with mouse support and additional actions such as jumping and crouching. The ViZDoom platform integrates mouse movement through two continuous actions = (c1, ). where ej and 22 correspond to horizontal and vertical aiming adjustments respectively. Furthermore, it allows for a combination of multiple simultaneous key presses, structured into multi-discrete action space D, formed by the Cartesian product of individual actions. This space includes 14 individual actions that can be activated in various combinations to generate total of D = 864 discrete actions. encapsulated in D {di, d2,.... d.4). ). The total action space is thus formalized as the product of these continuous and multi-discrete spaces:
To shorten the training loop and emphasize Safe RL principles over general RL challenges, HASARD provides the option to use a simplified action space for each environment, removing redundant actions that are not vital for solving the task and discretizing the continuous actions. For instance, in Precipice P Plunge, the agent can select from [ = 54 discrete actions, structured as A D A₁ X Ag x Ag x Ag, where A₁ (MOVE PORWARD, MOVE BACKWARD, NO-OP A = [TURN__LBS LEFT, TURN RIGHT, NO-OP), Ag = {LOOK_ _P, LOOK DOWN, NO-OP), and A4 (JUMP, NO-OP). This setting simplifies exploration and promotes faster learning while
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100N
1OOM
O
(a) Level features a simple layout (b) Level introduces obstacles and (c) Level presents additional chal- where the entire map is visible. The a complex terrain that obstructs the lenges with 1) decoy items that add agent's task is to collect weapons agent's view of the weapons and to the carrying load without offering within its carrying capacity and de- delivery zone, necessitating explo- rewards and 2) lethal acid pits that liver them to the starting zone, unim- ration. The JUMP action essential induce high cost if fallen into. The peded by any visual obstacles. The to traverse elevated surfaces. The JUMP action is effective to avoid pit- delivery zone is depicted in blue, floor and wall textures introduce a falls, but may not be feasible with making visually distinguishable degree of visual noise, making the heavy load, thus compelling the agent seek alternative routes.
from the rest of the map.
weapons harder distinguish.
Figure 3: Higher difficulty levels of Armament Burden incorporate novel task features.
preserving complexities of memory, short-term prediction, and spatial awareness. Appendix C.4 compares the performance between these two settings.
Rewards HASARD emphasizes safety over classic RL challenges like sparse rewards. The environments are designed such that off-the-shelf RL algorithms can achieve high rewards. Although rewards are relatively dense, agents not rewarded every time step (e.g., moving toward target yields no reward). Random exploration during early training will likely trigger reward signals-such such as collecting health item in Remedy Rush or firing rocket hostile unit in Collateral Damage. The rewards directly reflect the objectives listed in Table 2 offering the most long-horizon rewards since the agent must both acquire and deliver a weapon. Detailed reward functions can be found in Appendix
with A Armament Burden
A.2 A.2
Costs HASARD environments penalize greedy strategies by linking unsafe actions to costs. The severity of unsafe behavior is reflected in the cost signal, such as falling from greater heights in Precipica Plunge or harming more neutral units in Collateral Damage and Detonator's s Dimemma. In A Armaentt Burden, exceeding the carrying capacity not only incurs higher costs but also slows the agent, thereby altering the transition dynamics.
4.3 SAFETY CONSTRAINTS
Following the CMDP formulation in Equation each HASARD environment is governed by single safety constraint with an associated cost budget {c.). The default budget is empirically determined to pose a substantial challenge while allowing for some margin of error. In real-world applications, safety requirements vary widely. For instance, autonomous vehicle navigation must contend with unpredictable pedestrian behavior and sensor limitations, making perfect safety unattain- able; the goal here shifts toward minimizing unsafe behavior as much as possible. The adjustable safety budget in HASARD allows for balanced trade-off for such cases. Section 5.2 explores these adjustments. In contrast, scenarios such as nuclear reactor control require absolute precision with no margin for error. HASARD offers set of hard constraint environments where any safety violation triggers immediate consequences (see Appendix C.3
4.4 DIFFICULTY LEVELS
To ensure that HASARD remains challenging for future methods, each environment is designed with three difficulty levels. We increase complexity in two ways. First, by varying configuration parameters. For example, in Collateral Damage, reducing the number of enemies while increasing their movement speed, creates tougher challenge. Second, by introducing new mechanics. In Vo can Venture, Level features static navigable platforms, Level 2 changes the platform layout at set intervals, and at Level adds waggle motion. Figure the level mechanics of Armament Burden. See Appendix A.4 for more details on difficulty level modifications.

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Table 3: Rewards and costs of baseline methods averaged across final data points over five unique seeds. The green values indicate results satisfying the safety threshold, while bold values highlight the best reward achieved while staying within the cost bound. no method meets the safety threshold, the result with the lowest cost is highlighted instead.
Level
Armament Burden
Volcanic Venture
Remedy Rush
Collateral Damage
Precipice Plunge
Detonator's Dilemma
Method
f
C4
R
C4
f
C C4
f
C1
R 1
C4
R
C
PPO
9.68
109.30
51.64
172.93
50.78
52.44
78.61
41.06
243.42
475.62
29.67
14.28
PPOCost
5.47
3.67
30.73
5.44
28.21
6.75
68.71
24.04
237.72
0.69
27.60
8.45
PPOLag
7,51
52.41
42,40
52.00
36.37
5.25
29.09
5.61
147.24
44.96
21.49
5.62
PPOSauté
2.33
32.87
32.68
62.00
20.50
9.18
26.08
7.09
241.37
424.35
28.69
8.78
PPOPID
8.99
49.79
45.23
50.53
38.19
4.90
43.27
5.03
231.53
43,91
26.51
5.25
P30
8.60
40.72
43.55
46.10
37.90
4.78
46.52
5.97
242.53
176.41
29.62
6.59
PPO
4.24
99.59
38.31
186.44
61.94
62.22
53.58
61.13
324.69
608.07
40.09
19.63
PPOCost
7.59
6.20
21.10
3.93
0.01
0.03
21.86
8.19
162.01
61.19 40.37
15.56
PPOLag
4.50
53.50
26.11
53.10
28.75
5.72
18.87
5.61
105.01
52.39
19.57
5.34
PPOSauté
1.55
30.47
23.68
75.39
3.72
4.80
6.37
3.21
119.42
159.30
20.79
9.97
PPOPID
5.50
50.30
33.52
50.38
32.92
5.15
27.23
5.12
162.16
50.77 20.84
4.92
P3O
5,33
39.82
32.38
45,89
31.91
4.85
27.41
5.12
247.05
778.5625.45
6.97
PPO
1.99
118.22
42.20
347.77
53.16
68.27
34.86
84.44
487.17
894.22
49.36
23.72
PPOCost
0.04
0.05
10.61
14.76
0.01
0.02
3.93
1.35
15.39
6.64
49.95
19.79
PPOLag
2.03
31.89
22.77
52.54
8.02
9.74
12.22
5.29
23.87
34.98
19.27
5.26
PPOSauté
2.32
37.68
18.89
246.80
1.17
3.50
2.74
2.76
101.64
17..14
10.36
PPOPID
2.78
33.89
25.80
49.02
13.51
5.14
14.51
4.97
54.02
49.57
20.49
4.87
P3O
2.61
29.94
24.93
49.84
14.29
4.19
13.57
4.12
269.14
428.72
26.73
7.49
EXPERIMENTS
To demonstrate the utility of HASARD, we evaluate six baseline algorithms. 1) We employ PPO (Schulman al. 7)17) the unsafe baseline, which ignores costs. 2) We introduce PPOCost, a variant that treats costs as negative rewards. Engineering rewards in such a manner to satisfy cost constraints has many pitfalls (Roy al., 202 Kamran et al. 2022). 3) PPOLag (Ray al. 2019) is a well-known safe RL approach that uses the Lagrangian method to balance maximizing returns against reducing costs to predefined safety threshold. 4) PPOSauté (Sootla et al. 2022) uses state augmentation to ensure safety. 5) PPOPID (Stooke et al. (2200) employs a proportionallinter derivative controller to fine-tune the trade-off between performance and safety dynamically. 6) Finally, P3O (Zhang et al.) 2022) combines elements of PPO, off- off-polliy corrections, and dual-clip PPO objective to optimize both policy performance and adherence to safety constraints.
Protocol We run each experiment for 500 million environment steps using the simplified action space outlined in Section 442 repeated over five seeds. We utilize the Sample-Factory (Petrenko al., (020)) framework, which reduces the wall time of run to approximately two hours (see Appendix E.T. All experiments are conducted a dedicated compute node with 72-core 3.2 GHz AMD EPYC 7F72 CPU and a single NVIDIA A100 GPU. We adopt Sample-Factus Factory's 's default settings for our network configuration, PPO setup. and training processes. For more detailed experimental setup and exact hyperparameters please refer to Appendix B
5.1 BASELINE PERFORMANCE
Table shows that PPO maximizes rewards irrespective of costs, setting upper bounds on reward and cost in all environments. PPOCost finds reward-cost trade-off with no guarantee that costs stay below the threshold (note that we plot the original reward before cost deduction). We analyze scaling the cost factor in Appendix C.2 PPOLag closely adheres to safety thresholds, yet often yields the lowest rewards. The continuous adjustment of the Lagrangian multiplier causes fluctuations that prevent consistent constraint satisfaction. PPOSauté and P30 exceed the cost threshold in several environments, although P3O achieves noticeably higher rewards. Conversely, PPOPID consistently meets the constraints, most often outperforming other baselines, making it arguably the most effective method HASARD. The training curves presented in Appendix
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Armament Burden
Remedy
Reward
Reward
Cost
22
54
Steps
Steps
Steps
Steps
Volcanic Venture
Collateral Damage
200
Reward
Cost
Cost
43
23
Steps
3m
Steps
Steps
Steps
1
Precipice Plunge
Detonator's Dilemma
201
402
202
Reward
102
302
Reward
20
Cost
102
1.
Steps
Steps
Steps
Steps
Bound
Bound
Bound
Bound
PPOLag's performance under varying safety budgets on Level 1. PPOLag consistently adheres to the set safety thresholds, with tighter cost limits yielding lower rewards.
Figure 4:
5.2 VARYING SAFETY THRESHOLDS
The default safety bounds of HASARD environments offer complex challenge with some margin for error. As safety requirements vary real-world applications, these bounds are adjustable. To explore the effects of this, run PPOLag Level with higher and lower cost budget. Figure 4 shows that stricter cost limits result lower rewards, although in some tasks the reward changes are disproportionate the adjustments in the safety threshold.
5.3 AN IMPLICIT LEARNING CURRICULUM
Higher levels in HASARD not only feature pa- rameter adjustments to increase difficulty but also introduce new mechanics. When trained on the hardest level directly, the agent often struggles to learn a good policy. Training RL agents progressively more complex condi- tions to enhance learning efficiency has been widely explored (Florensa et al.) 2017]; |Fang et al. 2019). To investigate whether starting with easier tasks can lead better policies in HASARD, we evenly distribute the training bud- get across difficulty levels. We train PPOPID sequentially for 100M timesteps per level and compare the performance with an agent directly trained on Level for 300M timesteps. We select the Remedy Rush and Col lateral Damage envi- ronments because PPOPID exhibits significant performance gaps between levels, indicating the great potential benefit from the competencies developed in easier tasks. Table Ssows a nearly threefold performance increase in Remedy Rush and a 33% improvement in Co Collateral Damage over an equal number of timesteps. We find that leveraging the implicit curriculum from difficulty levels eases exploration challenges, prevents overly cautious behavior, and enables the agent acquire skills that direct training fails to develop. Research on integrating curriculum learning with safe RL. remains sparse. Previous studies (Bysenbach al. 2017 Turchetta et al.) 2020) have explored
Table 4: Difficulty levels of some HASARD envi- ronments provide successful curriculum to learn a safe policy more efficiently.
Remedy Rush
Collateral Damage
Training Regime
R
CJ1
R
C 4
Regular Curriculum
6.65
3.89
11.46
5,29
18.82
4.90
15.32
5.22
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(a) Accurately assessing spatial rela- (b) The segmented observation as- (c) The depth buffer assigns each tionships between objects and enti- signs each pixel predefined color pixel value from 0 (closest) to 255 can be difficult infer from raw based on its label: item, unit, obsta- (farthest). Intermediate values corre- observations.
cle, wall, or surface.
spond to relative distances.
Figure 5: To analyze the visual complexity of HASARD, create simplified representations through two strategies: (I) segmenting the observation and (2) including depth information.
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(a) Segmentation Boost: Training PPOPID on seg- (h) Depth Integration: Augmenting the RGB in- mented observations in Armament Burden Level 2 put with depth information in P Precipice Plunge yields 21% reward increase with similar costs, high- Level 2 results in 34% reward increase, underscoring lighting the benefit reduced visual complexity.
the importance accurate depth perception.
Figure 6: Impact of visual augmentations on learning.
this integration using specialized resetting agents manage task difficulty and safety. HASARD's inherent curriculum across difficulty levels opens new avenues for exploration in this field.
5.4 VISUAL COMPLEXITY
Noisy visuals make more difficuult for RL agents focus on the important aspects of the observations and effectively learn the task Hansen et al. (202T). While prior methods attempt to mask away the noise Bertoin al. 2022); Grooten et al. (2023), we simply create simplified visual representations of the input observation by augmenting it. We investigate whether the agent has an easier time learning the task from the augmented representations and determine whether the visuals of HASARD environments are visually complex and observations noisy. Figure 5 depicts the visual augmentations.
Segmentation ViZDoom provides a labels buffer with ground-truth labels for each pixel. We use these labels to map each unique object to fixed color to semantically segment the scene while ensuring consistent observations across frames. We hypothesize that this could help the agent perceive spatial relationships and better detect walls, surfaces, objects, and entities. We evaluate PPOPID on segmented observations Armament Burden Level 2, chosen for its varied textures and numerous objects (7 weapon types and 4 obstacle types, plus walls, surfaces, and the agent, totaling 14 unique labels). Figure (a shows a 21% reward increase with similar costs, indicating that segmentation simplifies learning.
Depth Buffer ViZDoom provides a depth buffer that assigns each pixel a value from 0 to 255, where 0 (black) indicates the closest objects and 255 (white) the farthest. Concatenating this additional channel to the RGB observation allows the agent to perceive spatial proximity directly. We evaluate PPO on Precipice P Plunge Level 2, where precise depth estimation plays key role in safely descending the cave. As shown in Figure (6b] this modification resulted in a 34% reward increase, underscoring the challenges of learning accurate depth perception from default observations alone.

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24p
(a) the early stages (b) The agent learns (c) To reduce costs (d) Refinement of the (e) The agent con- of training the agent to navigate the map beneath the safety safe strategy contin- contin-verges on a policy that randomly explor-with with a risky strategy, threshold, the agent ues as the agent op- consistently achieves ing. This leads to leading to high re- re-adopts a conservative timizes for higher re- high rewards without quent collisions with wards at the expense strategy by limiting wards while meaitain-exxeedingg the cost surrounding walls. elevated costs. its movements.
ing low costs.
budget.
Figure 7: Heatmaps of visited location frequency superimposed on the 2D map of Remedy Rush, illustrating PPOPID's policy evolution at key moments over 100M timesteps training on Level 1.
5.5 AGENT NAVIGATION
HASARD enables tracking the agent's ' visited locations during training. We aggregate the data from the 1000 most recent episodes and overlay it as a heatmap on the 2D environment map. This visual representation of the agent's 's movement patterns and exploration strategies provides insight into how the agent learns solve a task. Figure 7 depicts the progressive refinement of the PPOPID policy on Remedy Rush. Early stages show random exploration with frequent wall collisions, followed by greedy reward maximization, then a shift toward an overly cautious strategy, and finally convergence on refined safe strategy that achieves high rewards while keeping costs within limits. This analysis provides a qualitative basis to compare how different methods attempt to learn the task. We present further examples in Appendix C.5
6 CONCLUSION
Training competent agents able to navigate complex 3D tasks in safety-constrained settings remains challenge in RL. HASARD stands as a useful and cost-effective tool for embodied vision-based learning, extending beyond mere navigation tasks. Our experiments reveal that current state-of the art safe RL methods struggle to learn complex behaviors under safety constraints. We anticipate HASARD to facilitate the development of future methods, offering valuable insights and novel problems to solve. Its adjustable difficulty system allows to remain a valuable asset as safe RI. methods evolve, contributing to safer autonomous systems that interact with the physical world.
LIMITATIONS AND FUTURE WORK
The environments, though complex and visually diverse, are based on the ViZDoom game engine. Despite its advantages, it does not fully capture the detailed physics and nuanced realism of real- world settings. This limitation constrains the direct application of learned behaviors to real-life scenarios without significant adaptations or fine-tuning. Most actions for interaction in Doom are discrete, inherently simplifying challenges of accurate control. Only the full action space can effectively be used for multi- learning across environments to develop general agent capable of mastering all scenarios. Our environments only have a single objective and safety constraint. Future work could include multiple safety constraints, multi-objective tasks, collaborative multi-agent scenarios, and continual or transfer learning scenarios. We discuss these directions more in in-depth in Appendix G
ACKNOWLEDGMENTS
This work was conducted with the assistance of the Dutch national e-infrastructure, generously supported by the SURF Cooperative under grant EINF-8880. Additionally, this research was partially supported by the KOIOS project under grant agreement 101 101103770.
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A ENVIRONMENT SPECIFICATIONS
In this section, we provide further details of HASARD's environments.
A.1 DESCRIPTIONS AND MECHANICS
Detonator's Dilemma The agent begins with 100 health points and is equipped with pistol and unlimited ammunition to detonate the explosive barrels in the environment. Detonating a barrel grants +1 reward. A single bullet is sufficient detonate barrel, but missed shots may hit nearby neutral units. Given the barrels' fragility, chain reactions are easy to occur. An example is depicted in Figure Barrel explosions close units will harm them. When unit's HP reaches 0, it is eliminated, and the agent receives a penalty of -1. The agent is also penalized for harming itself, meaning that should keep its distance when detonating the barrels. On elimination, unit respawns at random location, so the number of entities remains constant. Some creatures carry weapons that can be collected upon their elimination and can be used to detonate barrels more efficiently compared to the pistol. This can be employed as a sacrificial strategy to destroy more barrels. Two neutral units each type are spawned at the beginning an episode at random locations throughout the map. The units vary in resilience: Lost Soul (10 HP), Zombi iMan (25 HP), ShotgunGur (40 HP), Cha ChaingunGuy (55 HP), DoomImp (70 HP), Demon (85 HP), and Revenant (100 HP). Level only includes Shot gunGuy, DoomImp, and Revenant. Level adds LostSoul and Cha i ingunGuy, while Level 3 incorporates all the listed creatures. The environment features seven designated patrol points, to which after every five seconds, each unit is randomly assigned one navigate towards. The patrol points are depicted in Figure
Figure 8: Detonating barrel may cause a chain explosion of adjacent barrels. The impact thrusts the agent backward and causes severe neutral casualties.
Figure 9: Patrol points of Detonator's s Dilemma. After every 5 seconds, each neutral unit is randomly assigned one of the seven depicted locations to navigate toward. This strategy ensures that units are almost constantly in movement, and makes their movement patterns more predictable.
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100%
100%
AHHO
HEALTH
BRHS
ARMOR
AHMO HEAL
ARHOR
(a) During the darkness interval, both health and penalty (b) Once the agent acquires the night vision goggles, it items become imperceptible. The agent has few strate gains permanent visibility, unaffected by the darkness. gies to consider: it could choose to remain stationary, However, the goggles render all items and surroundings avoiding the risk of collecting harmful items, or could less distinguishable by casting pervasive green hue memorize the positions of desirable items and navigate over them. While this change in color may seem minor by memory. Alternatively, the agent can seek night to human players, significantly affects image-hased vision goggles, which remain visible on the ground learning agents. Alterations color, saturation, or even in complete darkness, to maintain the ability to necessitate learning to act in diverse conditions. This introduces an interesting strategic trade-off.
discern the items.
Figure 10: Levels 2 and 3 of Remedy Rush impose an additional navigation challenge. The light in the main sector is periodically switched off.
Remedy Rush Many obtainable items are randomly distributed throughout the environ- ment at the beginning of each episode. The agent's objective ing items D+ = (HealthBonus, St iepiicc,eeikiiit and avoid penalty items D AArmorBonue Rocket Ammo, Shell, Cell]. Additional items are spawned at random loca- tions after every 120 in- ta-game ticks (~~ 3.5s): two HealthBonus and one of each type from D A new Stimpack, Medikit, and Infrared are spawned when picked up. By mastering precise controls, the agent can strategically leap over and avoid collecting unwanted items. In levels 2 and 3, the lighting of the environment alternates periodically between full brightness and complete darkness, adding layer of difficulty items become temporarily invisible. This effect is illustrated in Figure 10a The agent can find night vision goggles randomly spawned within the level, which allow for uninterrupted visibility despite the fluctuating lights, though they cast a strong green hue the surroundings (Figure 10b). Note that unlike other obtainable items, night vision goggles are visible on the ground during the darkness intervals.
collect health grant-
Armament Burden At the start of an episode, 10 random weapons are spawned at random locations. The agent can obtain the weapons by simply walking over them without performing any extra actions to pick them up. The agent's 's movement speed is dynamically adjusted based on the total weight w of the weapons carried. If w exceeds the carrying capacity c, the agent's 's speed is modified according to the formula:
max 0.1 Uo. t0
w
where to is the agent's initial speed. This mechanism ensures that the speed reduction is proportional the excess weight but does not drop below 25% of vo. thereby preventing total immobility. When the agent reaches the delivery zone, it regains its original movement speed 0g. Simultancously, the agent receives a reward based on the types and quantities of weapons delivered. Furthermore, the same number of weapons previously carried by the agent is respawned at random locations outside the delivery zone, with randomized weapon types. The agent can discard all carried weapons by utilizing the USE action. This can be strategically used to 1) lighten the load and restore the original movement, 2) avoid further penalties if the carrying load has been breached, 3) preemptively remove decoy items at key locations where they substantially inhibit the agent's 's movement, and 4) eliminate the weapons at the back of the area far from the delivery zone, hoping they would respawn closer. The latter two strategies impose potential opportunity cost, as the time spent discarding could be
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Table Obtainable items found in the Armament rewards and weights.
Burden scenario with their associated delivery
Super Shot-
Shot- gun
Chain- gun
Rocket Launcher
Plasma Rifle
BFG 9000
Blur Sphere
All map
Back pack
Rad
Item
Pistol
Suit
gun
Weight
0.1
0.25
0.4
0.55
0.7
0.85
1.0
0.25
0.5
0.75
1.0 0
Reward
0.1
0.25
0.4
0.55
0.7
0.85
1.0
0
0
used maximize rewards by delivering weapons. However, sacrificing this time could yield higher returns later in the episode. Higher difficulty levels introduce new features. In Level 2, weapons may not always be visible due to obstacles and complex terrain that obscure the agent's view. The agent can navigate more effectively with the use of the JUMP action. Level 3 introduces decoy items that increase the agent's carrying load without offering any reward for their delivery. This creates a challenge in credit assignment, as the agent cannot implicitly discern which items picked up contributed to the delivery reward. Table displays rewards and weights of all weapons and decoys. Another layer of complexity is introduced with the addition of acid pits. Each episode features 20 acid pits fixed size, randomly placed throughout the level. Falling into one these pits results in the agent losing all its health, accompanied by significant cost penalty.
The agent has to collect items in an environment, where percentage of random floor tiles are covered with lava. Armo Bonus collectible items are randomly distributed across the map, which may also appear the lava tiles. An additional item is spawned after every 60 in-game ticks (~ 2s). The agent starts with Hg = 1000 health points. Stepping on lava results in a loss of 1 health point per timestep. Higher difficulty levels have less of the surface Platform heights vary in Levels 2 and 3, and their locations change after a fixed time interval. When this occurs, the agent is granted short period of invulnerability to ensure that health loss and cost can entirely be avoided. Figure [ further describes the complexity introduced by terrain variations across levels. With hard constraints, the agent suffers severe penalty by losing all remaining health upon contact with lava, instantly ending the episode.
Volcanic Venture
covered by platforms.
1000% TEE
9967
FET
(a) Level features a layout where the environment (b) In higher levels, platform heights vary, creating lava surface partially covered with platforms, which challenging terrain to navigate. Their locations remain at fixed positions throughout an episode. also change regular intervals. Level introduces fur- agent's objective is to collect items that spawn continu- ther complexity by sporadically waggling the platforms ously while avoiding contact with the lava.
up and down.
Difficulty Levels in Volcanic Venture progressively increase complexity with dynamic and non-stationary terrain features.
Figure 11:
capable of firing rocket every eight frames. The task to eliminate hostile units (Cacodemon) without harming neutral units (Zombieman). The units spawn at random locations the other side of the environment away from the agent. A unit immediately respawns after being eliminated. The rocket's 's high area of effect (AoE) explosion presents extra safety challenge, illustrated in Figure] Due the projectile's travel
Collateral Damage The agent is armed with Rocket
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(a) The agent launches rocket di- (b) The target unit takes a direct hit (c) An adjacent neutral unit is rectly towards enemy unit. from the rocket.
harmed by the explosion.
Figure 12:
The explosion from the rocket has a high area of effect (AoE), potentially causing casualties even if the enemy unit has taken direct hit.
time across the environment, there is delay before impact, during which the entities' positions may change. The agent's ' distance from the units increases across difficulty levels. Higher levels further increase the challenge by featuring more neutral and less hostile units. Neutral units exhibit slow, random movements, adding an element of unpredictability. In contrast, enemy units move more swiftly between designated points the left and right sides of the map from the agent's 's perspective. Hostile unit speed increases with the difficulty level. In this scenario, the agent cannot move or harm itself.
9831
Figure 13: The ViZDoom game engine employs rendering technique called "non- non-perspective correct texture mapping" for vertical adjustments, leading to noticeable distortions when the player looks up down. This causes the textures and environment stretch or squash, resulting in fisheye effect (right). contrast, the more advanced GZ.Doom engine accurately renders views for vertical perception, avoiding such distortions (left).
Precipice Plunge In Level of the task, the agent must descend the cave by winding staircase formed by adjacent pillars, with each step having 24 vertical units lower than the previous. In Levels 2 and 3, the design changes: each row of pillars positioned 192 units lower than the one above, and the heights of individual pillars are randomized by 100 units, so the path down no longer resembles staircase. Level 3, the pillars further oscillate vertically-men -moving up and down by random amount between 128 and 512 in game units at random waggle frequency between 12 and 24, where higher values produce faster oscillations. As the agent descends deeper into the cave, the environment becomes progressively darker. The starting vertical height for the agent is denoted as hn O. For each subsequent row of platforms actual platform height at row & being h4 - A + rand $ L where rand[-, -| generates random variation within this range. The agent must avoid fall damage D, which is calculated by
the vertical height decreases by a fixed amount A, with the
(d 0) a, 00
if d
D
if
where d is the vertical distance fallen by the agent, 0 - 96 is the threshold for fall damage (below which no damage occurs), and 0.1 is the damage multiplier. It's important to note that the ViZDoom engine does not effectively support vertical aiming (looking upwards or downwards), resulting in heavy distortions, illustrated in Figure Although the ability to look up and down is essential for effectively completing the task, it introduces visual compromises due to the limitations of the engine.
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A.2 REWARD FUNCTIONS
In this section, we define how rewards R(I) are incurred at any given time step / in each environment.
Armament Burden R(t) Eiew, Th where We represents the of weapons picked up at time t, and is the reward associated with each weapon i picked up. The rewards for weapons are R (0.1, 0.25, 0.4, 0.55, 0.7, 0.85, 1.0), corresponding to the collection (Pistol, Shotgun, SuperShotgun, Chaingun, RocketLauncher PlasmaRi fle, BFG9000).
Remedy Rush R(t) =1 i + r2 - + m,, where 71 = 1.0, 12 = 3.0, 73 3 = 6.0 are the reward scalars for vials ne, stimpacks 8 and medikits my collected at time
Collateral Damage R(t) r R2, where r = 1.0 is the reward for eliminating enemy and na is the number enemies eliminated time t.
Volcanic Venture R(()= Ta, where 1.0 is the reward for collecting an armor bonus and the number of resources collected at time
Precipice Plunge R(t) max(0, 2t-1 2,), where 0.05 is positive constant scaling the reward for each unit depth reached, 2 the current z-coordinate of the agent, and 2 is the previous one.
Detonator's Dilemma R(t) = r- nt, where r = 1.0 is the reward for detonating barrel and na is the number of barrels exploded time f.
A.3 COST FUNCTIONS
In this section, we define how cost C(t) is incurred at any given time step in each environment.
Armament Burden (to (1 (p-1) (w, = 0)) max(0, (o. we-c) where W, is the collection of weapons carried at time step t, denotes the weight of weapon i, 1(.) is an indicator function, W; is a binary variable indicating whether weapon is obtained time L, p 0.1 is the penalty coefficient for carrying excess weight, and is the carrying capacity. The weights of weapons are W {0.05, 0.15, 0.3, 0.6, 1.0, 3.0, 6.0), corresponding to the collection (l, Shotgun. SuperShotgun, Cha nngun, constraint scenario, we employ (hara(t) H (Eiew, w; > c), where H 10 represents the hard constraint penalty applied instantaneously when the weight of the weapons exceeds the carrying capacity.
Plasm PlasmaRi EsmaRifle, fle, BFG9000). In the hard
Remedy Rush - (1e D-), where J, is the of items obtained at timestep D the set of incorrect items, and 1(-) an indicator function.
Collateral Damage C(t) = n,, where nu is the number neutral entities eliminated at time t.
Volcanic Venture
C(t) = 14,1 H1, where He is the agent's health at time step f.
Precipice Plunge C(t) H H4, where H, is the agent's health at time step t.
Detonator's Dilemma C(I) nu + a (H,-1 - H2). where n, denotes the number of neutral entities eliminated time H₁-1 and H, are the agent's health at the previous and current timesteps, respectively, and a = 0.04 is the health penalty scaling factor.
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A.4 DIFFICULTY LEVEL ATTRIBUTES
Table dispplys the difficulty attributes of each level.
Table 6: Level Difficulty Attributes of Environments. The attributes explained in Appendix A.1
Environment
Attribute
Level
Level
Level
Complex Terrain
Obstacles
X
Armament Burden
Pitfalls
Decoy Items
X
X
Health Vials
30
20
10
Hazardous Items
40
60
80
Remedy Rush
Darkness Duration
N/A
20
40
Night Vision Goggles
N/A
2
Hostile Targets
3
2
Target Speed
10
20
Collateral Damage
Neutral Units
5
Neutral Health
60
40
20
Distance From Units
256-456
400-600
544-744
Lava Coverage
60%
70%
80%
Changing Platforms
Volcanic Venture
Random Platform Height
Platforms Waggle
X
X
Step Decrement
24
128
192
Darkness Fluctuation
30
30
50
Precipice Plunge
Randomized Terrain
Moving Pillars
X
X
Creature Types
Detonator's Dilemma
Creature Speed
16
Explosive Barrels
10
20
B EXPERIMENTAL SETUP
We adopt most of our experimental setup from the PPO implementation for ViZDoom environments in Sample-Factio (Petrenko et al. 2020).
B. NETWORK ARCHITECTURE
The pixel observations from the environment are first processed by CNN encoder from (Mnih) et al.,22000 2015), incorporating ELU for nonlinearity. The convolutions passed through two dense layers, each with 512 neurons. GRU with 512 hidden units processes the sequential and temporal information from the environment. Afterward, the architecture splits into actor and critic network, sharing the same backbone. The actor produces categorical distribution of action probabilities for each single action set, while the critic outputs scalar value estimate for state-action pairs to guide policy improvements.
B.2 HYPERPARAMETERS
We present the extensive list of hyperparameters used our experiments in Table The hyperpa- rameters shared between our baselines.
B.3
IMPLEMENTATION DETAILS
Each action selected by the agent's 's policy is repeated for four frames, reducing the need for frequent policy decisions and conserving computational resources. During preprocessing. the RGB obser-
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Parameter
Value
Description
Batch Size (B)
1024
Minibatch size for SGD
Gamma (7)
0,99
Discount factor
Learning Rate (a)
1 x 10 10-
Learning rate
Hidden Layer Sizes
512, 512
Number of neurons the dense layers after convolutional encoder
RNN
GRU
Type of the RNN
RNN Size (h)
512
Size of the RNN hidden state
Policy Init Gain (g)
1.0
Gain parameter of neural network initialization schemas
Exploration Loss Coeff. (Ceap!)
0.001
Coefficient for the exploration component of the
loss function
Value Loss Coeff. (Ceal)
0.5
Coefficient for the critic loss
Lambda Lagrange (Magr)
0.0
Lambda coefficient for the Lagrange multiplier Change rate of the Lagrangian coefficient
Lagrangian Coeff. Rate (Flagr)
1 10
KL Threshold (0xL)
0.01
Threshold for the KL divergence between the old and new policy
GAE Lambda (GAA))
0.95
Generalized Advantage Estimation discounting PPO clipping ratio, unbiased clip version Maximum absolute change in value estimate un- til it clipped
PPO Clip Ratio (Colip)
0.1
PPO Clip Value (Aelip)
1.0
Nonlinearity (Ø)
ELU
Type of nonlinear activation function used in the network
Optimizer Adam Epsilon (EAdom) Adam Beta (31, (2)
Adam x 10 0.9, 0.999
Type of the optimizer
Adam epsilon parameter
Adam first and second momentum decay coeffi- cient
Max Grad Norm Policy Initialization
4.0
Max L2 norm of the gradient vector Neural network layer weight initialization method
orthogonal
Frame Skip
4
Number of times to repeat a selected action in the environment
Frame Stack
Number of consecutive environment pixel- observation stack
Env Workers Num Envs per Worker
32
Number of parallel environment CPU workers Number environments managed by a single CPU actor
Accumulate Batches
Max number training batches the learner ac- cumulates before stopping
Worker Num Splits
Enable double buffered experience collection, vector environment splits
Policy Workers per Policy
Number of workers that compute the forward pass for each policy
Max Policy Lag
1000
Beyond how many steps to discard older experi- ences
Table 7: Hyperparameters
vations are downscaled from 160 x 120 pixels to 128 x 72 pixels. We accumulate a maximum of two training batches a time, preventing data collection from outpacing training and maintaining balance between throughput and policy lag. We split an environment into two for double-buffered experience collection. Each policy has a designated worker responsible for the forward pass, and we maintain policy lag limit of 1000 steps ensure data relevance. Parallel environment workers are dynamically set based on CPU availability. The training cycle involves collecting batches to form a dataset size defined by the product of batch size and the number of batches per epoch, with standard batch size set at 1024. Rollouts are conducted over timesteps, aligned with the recurrence interval necessary for RNN policies, facilitating efficient data processing and learning accuracy.
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C EXTENDED BENCHMARK ANALYSIS
This section offers a more comprehensive examination of our baseline methods and the insights enabled by HASARD. We investigate how the full action space affects learning, assess the impact of hard safety constraints, explore PPOCost's sensitivity to cost scaling, compare baseline performances using different RL algorithms, and analyze how agents' navigation strategies evolve across different tasks.
C. RL BASELINE COMPARISON
All of our selected baselines in the main paper are extensions of PPO for safety. is important to note that the selected Safe RL methods are inherently independent of any particular base RL algorithm. However, in the original papers, the authors frequently utilized PPO methods. We therefore only included the PPO-based version not only because those have achieved higher results on prior benchmarks, but also not compare apples with oranges, i.e., not to conflate differences in performance due to the choice of the base RL algorithm.
base for their model-free
To demonstrate that the benchmark is not tied a particular type of base RL algorithm, we include TRPO-based Schulman et al. (2015) Safe RL method implementations which were included in the original papers. Table presents the results of Level 1. Although TRPO-based methods meet the safety threshold, they consistently yield lower rewards rewards-miroring the performance gap observed between the base PPO and TRPO implementations.
Table 8: Comparison of safety algorithms implemented on TRPO and PPO. The green values indicate results satisfying the safety threshold, while bold values highlight the best reward achieved while staying within the cost bound. If no method meets the safety threshold, the result with the lowest cost is shown instead. Results on Level show that the PPO-based implementations consistently outperform their TRPO-based counterparts.
Armament Burden
Volcanic Venture
Remedy Rush
Collateral Damage
Precipice Plunge
Detonator's Dilemma f
Method
f
C4
R 1
C4
f
C
f
C4
R 1
C1
PPO
9.68
109.30
51.64
172.93
50.78
52.44
78.61
41.06
243.42
475.62
29.67
14.28
TRPO
7.24
144.79
41.00
185.30
45.70
48.47
67.91
37.29
243.41
468.99
20.03
8.92
PPOLag
7.51
52.41
42.40
52.00
36.37
5.25
29.09
5.61
147.24
44.96
21.49
5.62
TRPOLag
4.06
48.61
30.48
49.91
21.69
4.98
31.63
7.28
177.93
309.16
16.84
6.85
PPOPTD
8.99
49.79
45.23
50.53
38.19
4,90
43.27
5.03
231.53
43.91
26.51
5.25
TRPOPID
0.36
8.18
23.81
50.93
10.36
4.54
34.74
10.13
172.87
299.46
12.81
6.84
C.2 COST FACTOR SENSITIVITY
The PPOCost baseline, while simple and straightforward in approach, has demonstrated its potential in our experiments. On Level of Precipice P Plunge, it achieved nearly zero cost while securing the highest reward among methods that adhere to the safety threshold. Similarly, in Level 2 of Armament Burden, PPOCost again obtained the highest reward among all methods compliant with the given budget. However, its effectiveness hinges on determining the appropriate cost scaling factor, which requires manual tuning through a time-consuming and costly trial-and-error process. Consequently, we explore how sensitive PPOCost is to variations in the penalty scaling factor. We evaluate the cost scaling values of 10 0.5, 1.0, 2.0] on Level I of all environments. Note that in the main experiments, Figure 14 and the evaluation results in Table We can observe that the reward and cost are tightly bound: reduction of cost necessitates reward sacrifice. Note, that we introduced PPOCost proof of concept to demonstrate this direct trade-off in our environments along with the extent to which cost can function as negative reward the benchmark.
arbitrarily chose coefficient of 1.0. We present the training curves in
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Table 9: Performance metrics of PPOCost across varying cost scales.
Armament Burden
Volcanic Venture
Remedy Rush
Collateral Damage
Precipice Plunge
Detonator's Dilemma
Cost Scale
f
C4
C
R R
C
f
C4
R
C4
R
16.40
9.09
128.61
92.23
94.94
50.49
109.61
41.34
609.65
408.35
35.27
13.25
0.5
3.86
1.64
42.30
19.13
57.04
23.06
86.67
33.23
441.61
399.14
27.93
10.28
1.0
0.13
0.10
25.93
6.00
18.30
6.03
64.79
24.60
196.93
2.29
18.39
6.37
2.0
0.00
0.04
17.64
2.12
0.00
0.03
30.41
8.55
203.46
1.20
0.57
0.20
Armament Burden
Volcanic Venture
125
102
Reward
Roward
102
1.
Cost
Steps
Steps
Stops
Steps
Remedy Rush
Collateral Damage
53
Reward
62
Rowardd
Steps
340
Steps
160
Steps
Precipice Plunge
Detonator's Dilemma 15.7
Reward
802
Cost
203
201
Steps
Steps
Steps
Steps
Cost Scaler 0.1
Cost Scaler 0.5
Cost Scaler 1.0
Cost Scaler 2.0
Figure 14: PPOCost treats costs negative rewards without providing general method for scaling these costs relative to rewards. Training with various cost scaling values indicates that PPOCost is highly sensitive to this parameter, resulting in wide range of reward-cost trade-offs. For the main results of the paper, we adopted a scaling factor of 1.0, ensuring equal weight between rewards and negative costs. With extensive manual tuning, PPOCost can find diverse trade-offs, however, it cannot satisfy given safety budget.
C.3 HARD SAFETY CONSTRAINTS
Under hard constraints (E 0), any action that violates safety leads to of two outcomes: (I) termination of the episode, where the trajectory is deemed a failure, all rewards are withheld, and substantial costs
imposed. This approach redefines the episode's time horizon T min(! (2 (4) > 0}, effectively shortening the episode to the point of the first safety violation. Alternatively. (2) it results in severe in- game penalties that drastically reduce progress and potential outcomes. For instance, in Armament Burden, the agent loses all obtained weapons when exceeding the carrying capacity. Figure 15] illustrates the effects of soft and hard constraints in selected game scenarios, demonstrating the impact of each constraint type on gameplay dynamics and strategy. This experimental setting highlights the importance of how safety constraints are integrated and evaluated within RL training regimes. Whereas the default soft constraint setting assumes there is a cost budget in the bounds of which the agent can navigate, certain applications are more safety-critical Dealing with the potential of crashing car into wall presents very different requirements. As there are several known formulations of safety in RL (Wachi et al. (0246), is important to acknowledge what type of safety problem algorithm designed to solve. With this
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Armament Burden: Upon exceeding capacity soft constraint slows the agent, allowing continued (a) Ve1 canic Venture: Under the soft constraint, the agent can step on the lava to collect items or reach other platforms, albeit at a health cost. Under the hard constraint, any contact with lava results in immediate termination of the episode due total health loss.
progress with reduced speed, whereas the hard con- straint causes the agent to drop all weapons, forcing restart of the collection process.
Figure 15: The hard constraint setting affects the agent's progression more severely.
setting, HASARD therefore aims provide versatile benchmark that facilitates evaluation across different safety formulations.
Table 10:
Performance on Level I of the HASARD benchmark with hard constraints across five unique seeds. We show the average of the final ten data points. Maximum rewards and minimum costs are highlighted in bold.
Armament Burden
Volcanic Venture
Remedy Rush
Collateral Damage
Precipice Plunge
Detonator's Dilemma
Method
R
C1
R
C4
R
C4
R t
C
R 1
C1
R f
CJ
PPO
13.97
0.70
15.47
1410
18.09
8.28
10.12
9.40
38.40
10.00
3.73
12.52
PPOCost
5.19
0.03
0.65
15.83
0.01
0.21
0.47
0.55
37.00
10.00
0.67
0.87
PPOLag
0.01
0.00
1.15
12.49
0.01
0.19
0.15
0.54
194.4
0.65
0.03
0.13
has been shown that in environments with hard constraints, Safe RL agents suffer from the safe exploration problem (Garcia & Fernández 2012|Pecks & Svoboda 2014). We observe the same issue as indicated by the results in Table I0 The penalty for risky exploration is prohibitive, pushing PPOCost and PPOLag towards overly cautious behaviors, as evidenced by their near-zero rewards. Precipic ce P1 Plunge the only environment, where PPOL aag manages to learn a safe policy, allowing it to navigate to the bottom of the cave without falling single time. Consequently, it outperforms PPO and PPOCost, which only earn a small reward for their initial leap, as the episodes terminate abruptly upon incurring fall damage. Interestingly, PPO learns to neglect heavier weapons in the Armament Burden scenario without any explicit cost signal. This behavior appears to response to the lack of rewards associated with these weapons, exceeding the carrying capacity results in the loss all acquired weapons. This allows PPO to achieve returns similar to those of the soft constraint setting. potential approach developing successful policy involves using curriculum that progressively reduces the cost budget until it reaches zero. We leave this open for future research.
C.4 FULL ACTION SPACE
The full action space incorporates the following array of actions: D MOVE_FORWARD, MOVE_ BACKWAR,, MOVE_RIGHT, MOVE_LEFT, SELECT_NEXT__EAE
SELECT CULECC_PREVV_S WEAPON, ATTACK, SPEED, JUMP, USE, CROUCH, TURN180, _IIHTT-DETTAA__CCTCCCCIITIICC DELTA }. Some actions, such as USE, only have an effect in certain environments like Armament Burden and Precipice Plunge. In
LOOK_UB UP DDWD _DLL_,EEE,,,, TURN LEFT BIGHT
Table 11: Action space comparison between simplified and full configurations.
Actions
Discrete
Continuous
Simplified
6-54
0
Original
864
2
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Armament Burden
Venture
120
Rewrrdd
Reward
Cost
23
12
Steps
Steps
Steps
Steps
Remedy Rush
Collateral Damage
Reward
Reward
33
13
12
18
SSrety Tirestord
1000
Steps
3m4
Steps
Steps
Steps
1.
Precipice Plunge
Detonator's Dilemma
Rewaard
302
Reward
Cost
303
100
Steps
Steps
Steps
Steps
Simplified Space
Space
While the complete DOOM action space potentially facilitates more efficient task resolu- tion with a wider array of actions at the disposal of the agent, PPOLag fails leverage this advantage, even at Level I of HASARD. Although PPOLag consistently meets the default safety budgets across environments, it significantly lags behind in achieving rewards comparable to those obtained using a simplified action space. The full DOOM action space therefore presents much more complicated Safe RL learning problem.
Figure 16:
Table 12: The full action space setting presents a far greater challenge to PPOLag Level 1.
Armament Burden
Volcanic Venture
Remedy Rush
Collateral Damage
Precipice Plunge
Detonator's Dilemma
Action Space
R f
C4
R
C4
R 1
C4
R
C4
R
C4
R
C
Simplified Full
7.51
52.41
42.40
52.00
36.37
5,25
29.09
5.61
147.24
44.96
21.49
5.62
4.97
52.74
26.43
49.58
24.96
9.26
14.03
4.85
40.84
76.41
4.98
4.42
other scenarios, such actions redundant, adding extra overhead for the agent discern their irrelevance. Other actions could offer quicker alternatives for achieving certain objectives. For example, MOVE MOVE_LEFT LEFT directly allows sidestepping to the left, whereas sequence of TURN_ LEFT + MOVE_ MOVE_FORWARD ORWARD - TURN_RIGHT accomplishes the same, however much slower. Therefore, in theory, each task could be solved more effectively with the full action space; however, its complexity presents more challenging learning problem. To demonstrate this complexity, we run PPOLag on the easy level of all environments with the full action space. We present the evaluation results in Table and and the training curves in Figure 16 For fair comparison with the original results, we restrict movement in Col collateral Damage and acceleration Armament Burden. PPOLag adheres the default safety budget when using the full action space, but experiences significant reduction in reward. We have made the use of the full action space configurable option, allowing benchmark users to tailor it according to their needs.
C.5 EXTENDED AGENT NAVIGATION
This section presents additional examples of insight into agent behavior during training via heatmap visualization of spatial visitations. There numerous policies that, despite exhibiting distinet
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behaviors, could result in the same reward and cost outcomes. This visualization offers window into the qualitative evolution of agent behavior. We can observe how initially random exploration gradually gives way more deliberate navigation patterns as the agent learns to balance reward-seeking with safety constraints. This not only validates performance metrics but helps in understanding how agents internalize the task objective and safety constraints. Such insights help diagnose potential inefficiencies or overly cautious behaviors in different algorithms and guide further refinement.
Figure [7p shows how PPO opts for direct leap down the Precipice Plunge cave, while the PPOPID agent has learned to follow the winding path to mitigate fall damage. Figure [88 depicts how increasing difficulty levels inhibit the agent's movement from the delivery zone in A Armament Burden. The heatmaps of Det onator" s Dil emma in Figure reveal that the agent prefers to cover different sections of the map during different stages of training as its policy evolves. Finally, Figure 20) compares the unsafe movement patterns of PPO in Volcanic Venture with the PPOPID agent optimized for safety.
Step. 10979508E
Step: 1091712G4
(a) The PPO agent plunges straight down the precipice, (b) The PPOPID agent has learned to follow the winding quickly reaching the bottom and neglecting fall damage. path down the with minimal fall damage.
Figure 17:
Heatmaps of visited location frequency superimposed on the 2D map of P Precipice Plunge after training 100M timesteps Level 1.
Step: 0922
Step: 1068301
Step: 106848256
(a) Level 1 has an almost flat sur-(b) Level 2 features a more intricate (c) Level 3 adds acid pits and decoy face, allowing the agent to navigate terrain with obstacles, challenging items that significantly constrain the freely and cover most of the area for the agent's ability to locate weapons agent, limiting its movement primar- and maneuver effectively.
weapon delivery.
ily to the starting area.
Figure 18: Heatmaps of visited location frequency superimposed on the 2D map of A Armament Burden after training PPOPID for 100M timesteps.
SCAP: DOSSS1
Step. PAD REUZE
Stap 10016 7680
(a) Early in training, the agent con-(h) Midway through training, the fo-(c) By the end of training, the agent centrates its exploration around the shifts towards the lower region consistently favors the bottom-right center of the map.
of the environment.
Figure 19: Heatmaps of visited location frequency during PPOPID training on Level 2 of Detonator's s Dilemma over 100M timesteps, illustrating the agent's evolving spatial focus.
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Step: 100896768
Step: 102100992
(a) The PPO agent repeatedly circles environment (b) The PPOPID agent frequently traverses central to maximize item collection while neglecting lava. area to avoid lava.
Figure 20:
Heatmaps of visited location frequency superimposed on the 2D map of Vol canic Venture after training 100M timesteps on Level I.
D SIGNIFICANCE OF HASARD
HASARD's discrete simplified physics and pixelated graphics do not mirror the high-resolution imagery and complex dynamics required for autonomous driving or assistive robotics. Naturally, an agent excelling in HASARD would not be anywhere near capable when deployed in any of the aforementioned real-life scenarios. Nevertheless, there substantial value in utilizing unrealistic simulation environments for foundational research in Safe RL. We list several key points motivating HASARD's s design and utility:
Computational Accessibility Incorporating highly realistic physics and visual rendering signifi- cantly increases the computational cost of simulations. HASARD is designed be accessible for low-budget research settings, allowing more researchers to engage with vision- based Safe RL. Train- ing RL algorithms in ultra-realistic environments with accurate physics and complex decision-making problems is not only costly but also time-intensive. By maintaining balance between realism and computational demand, HASARD enables tight feedback loop that facilitates rapid experimentation and iteration.
Focus Vision-Based Safety Much of recent Safe RL research has predominantly concentrated on continuous control problems, exemplified by the widely-used environments in Safety-Gymnasinn J] al. (023a). HASARD aims bridge the gap in safety considerations within vision-based learning. a domain with wide range of applicability. The egocentric embodied perception HASARD environments further introduces the problem of partial observability, which is often absent in prior works. The ultimate goal for many applications is to enable complex decision decision-making and precise manipulation under realistic physics, leveraging multimodal inputs of visual imagery and LIDAR. However, fulfilling all these criteria simultaneously poses a significant challenge for Safe RL research. To manage this, HASARD narrows the focus and decouples the problem, concentrating on visual perception of 3D environments, spatial awareness, and high-level control.
Precedent in Research Video games, despite their lack of realism, have been instrumental in advancing Al research (Hu et al. 2024). They provide a controlled yet challenging environment for adopted useful tools within the community (Chevalier- Shhevelier-Boisvertt et al.) 2024 Gong et al., 2023) Jeon et al.) (023). supporting the development of algorithms that can later be adapted to more realistic applications.
developing and testing methodologies. Many game-base based 3D benchmarks
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Similarly, simulation environments in Doom remain viable and relevant, as they have been widely used in recent research (Park et al. 2024, Kim et al. 2023 Zhai et al. 2023 Valevski et al. (024).
Solving Toy Problems Simulation environments with simplified physics and visuals roughly emulate critical aspects of more complex systems. For example, much recent Safe RL research utilizes gridworld environments (Wachi et al., 2024aa Den Hengst et al. 2022), which, while basic, allow for the exploration of key concepts and strategies in safety and exploration. Similarly, HASARD, with its lower-resolution visuals and basic physics, still captures essential elements of navigating in a 3D space and introduces significant challenges related to egocentric Safe RL, such as depth perception, short-term prediction, and memory.
E COMPARISON WITH
Sifety-Gymmaiimm is the most comprchensive and closely related benchmark to work, making it an essential point of comparison. Although it consists of 28 environments, there are strong similarities between them. For instance, the six environments in the Safe Velocity suite all share the identical objective of the agent moving forward and are visually identical. These could effectively be considered a single environment with variations in the robot type.
Similarly, the four tasks in the Safe Navigation suite featuring different robots like Point, Car, RaceCar, Doggo, Ant share single navigation objective and only slightly vary the objects within the environment. We argue that the variations within a single envi- ronment in HASARD offer equal or greater diversity than the differences observed among the Goal, Button, Push, Circle tasks in Safety-Gymmasium.
The Safe Vision suite introduces more visually diverse settings with environments such as Building, Race, FormulaOne, aOne. However, the core tasks remain focused on navigation, and higher difficulty levels simply introduce additional obstacles. Furthermore, these environments lack dynamic elements. The few entities in the Bu l lding task move in predictable, fixed patterns, and the environment remains static throughout the episode. The only changes observed are those directly caused by the agent.
In contrast, HASARD extends beyond mere navigation-basee tasks, necessitating higher-order reasoning for task resolution. It incorporates randomly moving units, and exploits the third dimension more effectively, enabling entities to navigate vertical surfaces, resulting in more complex dynamics. Leveraging the ViZDoom game engine, HASARD allows for rapid environment simulation, while offering a broad and dynamic challenge that provides a comprehensive evaluation of many agent competencies, such memory, short-term prediction, and distance perception.
E.1 FRAMEWORK EFFICIENCY
To demonstrate the training efficiency of HASARD, we conducted a comparative analysis with Eefett-Gymmasiumm (Ji et al. 2023a) on the same hardware. Among existing benchmarks tailored for Safe RL research, Safety- Gyntasiumm is arguably the most comprehensive suite and closest to our work, as it uniquely facilitates -isied learning within 3D environments. We arbitrarily 1-00 task and ran the PPOLag implementation with default settings in Omnisafe {ji ct al. 2023b), a popular recent Safe RL library. We allowed two hours of training time for each framework. In Figure 21 and Table 13 we compare how many environment iterations is the framework able to facilitate and how frequent are the policy updates. We can see that HASARD outperforms Safety Gymnasium in both metries by a large magnitude. Note, that HASARD runs with Cumple-Factory out-of-the-bee whereas integrating Lafety--yymasee with Sample-Factors would require substantial effort.
selected the SafetyPoint
Table 13: Efficiency Comparison of Benchmarks
Benchmark
Frames Per Second
Updates Per Second
Safety-Gymmnt Gymnasium
180
0.03 15.1 12
HASARD
53985
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Cumulitive Framess (lag scale)
10a
10a
107
Cumulativc Updates (log
20
10
HASARD
HASARD
10a
Safet
10a
Safely-Goe Geyy-Gymmnsium
100
120
Time (minutes)
Time (minutos)
(a) HASARD is capable of accumulating frames (h) The lack of effective parallelization results in CPU a rate several magnitudes greater than Safety- rollout bottleneck due to which Safety-Gymnasiun Gymnasium on the same hardware.
achieves far fewer policy updates.
Performance comparison of benchmarks over hours of training on identical hardware. We trained a PPOLag agent on safetyyointGoall-- from Safety-Gymmasium using default configurations.
Figure 21:
l-v0 from HASARD and
Table 14:
Human player rewards and costs averaged over 10 episodes compared to the PPOPID results with the simplified action space.
Level
Armament Burden
Volcanic Venture
Remedy Rush
Collateral Damage
Precipice Plunge
Detonator's Dilemma
Method
R
CJ
R
C
R
C4
R
C
R t
C
R
C
PPOPID
8.99
49.79
45.23
50.53
38.19
4.90
43.27
5.03
231.53
43.91
26.51
5.25
Human
18.60
42.83
57.12
45.97
51.04
5.65
42.33
4.11
243.09
46.88
31.54
4.49
PPOPID
5.50
50.30
33.52
50.38
32.92
5.15
27.23
5.12
162.16
50.77
20.84
4.92
Human
16.85
31.50
42.91
45.09
34.72
1.22
17.34
3.10
226.11
44.35
34.66
3.09
PPOPID
2.78
33.89
25.80
49.02
13.51
5.14
14.51
4.97
54.02
49.57
20.49
4.87
Human
9.00
31.40
35.27
49.81
36.91
4.22
16.40
4.99
104.62
21.54
37.10
4.08
F THE HUMAN BASELINE
The question arises whether HASARD environments are already solved. Since the optimal strategy unknown, cannot establish absolute performance ceiling. Although PPO, which ignores costs, might serve reasonable upper bound for reward, our focus is on the highest reward achievable under safety constraints. To address this, we introduce a human baseline, similar the approach in the Atari benchmark MMih et al. (013). We record the reward and cost of human player averaged over 10 episodes per environment. Table compares human performance with PPOPID, algorithm. Although the human player has access to the full action space of Doom, we compare the PPOPID results obtained with the simplified action space. As demonstrated in Appendix C.4 this setting yields far more favorable outcomes. The results indicate that PPOPID only achieves human-com cuman-comparable performance on Co Collate arraall Damage, suggesting that other tasks still far from being solved. It should be noted that the human baseline likely to be suboptimal, implying ample room for improvement.
F.1 WHAT MAKES HASARD COMPLEX?
In many instances at higher difficulty levels, the human player reached a higher score not due to more precise control, but through strategies that PPOPID and none of the other baselines managed to learn. After all, while the RL method can consistently output precise actions for each frame, the human is playing in real time ticrate of 35, leading to longer reaction times.
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In Armament Burden, the agent has the option to discard all weapons. This strategy be used to get rid of the very heavy weapons far from the starting zone, since carrying them back can be very costly. None of our evaluated baselines managed learn this behavior. Instead, they simply avoided picking up heavy weapons altogether, as discarding weapons does not provide an immediate obvious reward. Level introduces decoy items that add to carrying load but do not grant any reward when delivered. None of the methods were able to figure out to avoid these items.
In Levels 2 and of Remedy Rush, the environment becomes periodically dark after short time intervals, limiting the agent's ability to see the items it needs to collect or avoid. However, night vision goggles are located in the map, granting permanent vision when obtained. Despite this, none of the baselines developed consistent strategy to seek out the goggles early in the episode before proceeding to with their item collection.
Level 3 of Det Detonator's s Dil Dilew emma allows the agent to push the barrels to safer locations with few or no units nearby before detonating them. However, since pushing barrels does not yield immediate rewards, the baseline methods failed to adopt this strategy. Instead, the agents tend to avoid going near the barrels, as detonating them while standing too close often results in the agent harming itself and incurring cost.
G EXTENDED FUTURE WORK
In this section, we will elaborate on several extensions to broaden the benchmark's application for other settings.
the current HASARD framework, each environment only has a single safety constraint. Given the focus on Safe RL, incorporating multiple safety constraints presents com- pelling avenue. In the current version only Detonator's s Dilemma, has two factors that increase the cost: (1) the agent harming neutral units and (2) the agent harming itself. Instead of combining them into one single value, we could decouple them into separate safety constraints. Similarly, a second constraint could be incorporated into Armament Burden for how frequently the agent is allowed to visit the delivery zone, and into Vol Volcanic Venture for how often the agent can use the JUMP action.
Multi-Constraim
The ViZDoom platform supports synchronous operations among multiple agents, allowing HASARD to be extended to collaborative MARL scenarios. Precipice Plunge does not provide any meaningful ways of collaboration, but in other environments multiple agents could collaborate by dividing the workload of the task, focusing on separate areas of the map.
Multi-Agent
Caanstrr/CCttinnaa/MMittttttt Learning The full action space setting is unified across all HASARD environments, allowing agents to there are many common elements, such as spatial navigation, entity behaviour, and environment dynamics. This paves the way for training a versatile agent capable of mastering all six tasks across three difficulty levels. This can be done in a multi-task or continual learning setting. A further inter- esting area of exploration is to what extent learned competencies are transferable across environments. For instance, the ability to successfully navigate an area without colliding with walls could be shared across tasks.
consistent policy across all tasks. Moreover,
H TRAINING CURVES
Figure 23 depicts the training curves for Level 1. PPO maximizes the reward irrespective of associated costs, setting an upper bound for reward and cost all environments with unconstrained behavior. The primal-dual Lagrangian PPOPID and the penalty-based P3O manage to obtain the highest rewards while adhering to the default cost budget. PPOCost treats costs as negative rewards, occasionally yielding reasonable outcomes but without any guarantee of adhering to safety constraints. PPOLag closely meets the safety thresholds with some fluctuations, yet frequently yields lower rewards. PPOSauté has varied performance across tasks, often failing to satisfy the cost threshold.
In most tasks of Level 2, PPO also sets the upper bounds for reward and cost, as seen in Figure 24 PPOLag is the only baseline that consistently maintains the accumulated cost near the safety bud-
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get. Conversely, PPOSauté struggles to lower its cost and stay within the safety budget in several environments.
As shown in Figure 25 PPO consistently dominates with high rewards accompanied by high costs also Level 3. PPOLag and PPOSauté are unable to improve rewards while effectively controlling the cost in Remedy Rush, Collateral Damage, and Precipice Plunge, the curves remain relatively flat. Remedy Rush demonstrates the fluctuating behavior of PPOLag originating from the Lagrangian optimization. Notably, PPOCost manages obtain equal rewards PPO in Detonator's s Dilemma while maintaining a lower cost.
Figure 22 shows the training curves for the hard constraint setting of level 1. PPO maintains high returns due to lack of an explicit safety feedback mechanism. In contrast, PPOCost and PPOLag exhibit overly conservative behavior by consistently selecting the passive NO-OP action, failing to learn policy that achieves noticeable rewards while strictly adhering to safety constraints. Note that the cost threshold is under hard constraints.
Armament Burden
Volcanic Venture
Roward
Roward
Cost
1202
V92
2.5
6.2
253
6.0
0:0
Steps
Steps
Steps
Steps
Remedy Rush
Collateral Damage
12.5
Reward
15
Cost
Reward
10.2
368
105
Steps
143
Steps
1:5
Precipice Plunge
Detonator's Dilemma
12
22.5
192
Reward
Cost
Roward
Cost
20.3
102
5.3
2.5
Stops
Steps
Steps
Steps
PPO
PPOCost
PPOLag
Figure 22: Training curves of HASARD Level under hard constraints with 95% confidence intervals across five seeds.
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Published as a conference paper at ICLR 2025
Armament Burden
Volcanic Venture
302
Reward
100
360
Stops
169
Steps
Remedy Rush
Collateral Damage
Reward
Cost
22
13
Steps
Steps
Stops
Steps
Precipice Plunge
Detonator's Dilemma
207
15.2
23.5
Reward
1:3
Cost
302
Sarey
101
Steps
Steps
160
Steps
PPO
PPOCost
PPOLag
PPOSaute
PPOFID
P30
Figure 23: Training curves HASARD Level with 95% confidence intervals across five seeds.
Armament Burden 402
Volcanic Ventuuee
203
Reward
153
22
200
143
Stops
143
Remedy Rush
Collateral Damage
53
Reward
Cost
Cost
32
13
Stops
Stops
Stops
Stops
Precipice Plunge
Detonator's Dilemma
302
42
Reward
Reward
153
300
Stops
143
Stops
PPO
PPOCost
PPOLag
PPOSaute
PPOPID
P30
Figure 24: Training curves of HASARD Level with 95% confidence intervals across five seeds.
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Amaaent Burden
Volcanic Venture
302
Rewerd
100
Sarety Tivenoo
360
Steps
143
1e5
Remedy Rush
Collateral Damage
Reward
Cost
Cost
23
Stops
Stops
Stops
Stops
Precipice Plunge
Detonator's Dilemma
Rewarrs
307
502
NM
Reward
802
102
201
Sarely
Steps
1.4
Steps
PPO
PPOCost
PPOLag
PPOSaute
PPOPID
Figure 25: Training curves of HASARD Level with 95% confidence intervals across five seeds.
33


Reviews:
[{"review_id": "jYhWT8bnG0", "reviewer": "Reviewer_FzXY", "summary": "The paper presents HASARD, a benchmark tailored to vision-based safe reinforcement learning (RL) using egocentric, pixel-based inputs. Built on the ViZDoom platform, HASARD comprises six unique 3D environments across three difficulty levels, each designed to test safe RL in increasingly complex and dynamic scenarios. The benchmark allows for a range of agent objectives, from navigation to item collection and hazard avoidance, focusing explicitly on embodied safe RL with vision-based inputs.", "questions": "1. Which tasks in HASARD require memory capabilities, and which involve long-horizon decision-making? It would be helpful if the authors could clarify how the benchmark challenges an agent’s memory and planning capabilities over extended time sequences.\n\n2.  Why did you choose ViZDoom to build this benchmark? Does this platform offer specific advantages? From my perspective, it seems that ViZDoom allows only minor modifications to its existing game structure and may lack the flexibility to define more complex, varied tasks. Why not consider using a truly open-world environment, such as MineDojo [4], which enables safer RL environments with more sophisticated task definitions? A platform like MineDojo could potentially support a broader range of scenarios and facilitate more diverse task creation.\n\n3. Additionally, I noticed that you used Omnisafe for algorithm benchmarking, but this wasn’t mentioned in the paper. I have some questions regarding one of the baselines you implemented. In the P3O algorithm code (see here:https://github.com/PKU-Alignment/omnisafe/blob/main/omnisafe/algorithms/on_policy/penalty_function/p3o.py#L82), there is a term  J_c  in the loss function that appears to be independent of the network parameters. What effect does including J_c in the loss function have? I observed in your experimental results that P3O also fails to satisfy the constraints, which may be related to the J_c term. This raises some doubts about the effectiveness of this baseline.\n\n[4] MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Diverse Scenarios: HASARD provides varied 3D environments with different objectives and challenges, such as item collection, navigating hazardous terrain, and avoiding neutral units. This variety enriches the learning and testing possibilities, ensuring that the benchmark assesses both task performance and safety considerations.\n\nStructured Curriculum: By offering three difficulty levels, HASARD presents a built-in curriculum for training RL agents, allowing gradual learning in increasingly challenging conditions. This approach is effective for developing robust agents that can generalize to new, more complex scenarios.", "weaknesses": "Outdated Baselines: All the baseline algorithms were published over two years ago, and the original implementations of these baselines do not support visual inputs. The lack of SOTA vision-input baselines, such as Lambda [1], Safe SLAC [2], and SafeDreamer [3], limits the benchmark’s relevance in evaluating current state-of-the-art safe RL methods.\n\nSolvability by Existing Algorithms: Have the tasks introduced in this framework already been solved by existing algorithms? For instance, can PPO-PID successfully address these tasks? Are there settings within HASARD that current algorithms struggle to handle? By not including experiments with the latest baselines, it is unclear whether the HASARD benchmark will drive the development of new algorithms or simply reaffirm existing solutions.\n\nTask Complexity:\nWhat is the primary contribution of HASARD compared to existing safety benchmarks, such as Safety Gymnasium? Compared to Safety Gymnasium, HASARD primarily adds hard constraints and fast simulation. However, implementing hard constraints is relatively straightforward, merely requiring a single line of code to terminate the episode upon any unsafe action. As for fast simulation, HASARD achieves this by sacrificing simulation fidelity and simplifying the action space, which limits its meaningfulness as a contribution compared to Safety Gymnasium.\n\nMoreover, most tasks in HASARD revolve around avoiding hazardous obstacles, which has already been extensively addressed and solved in Safety Gymnasium by existing algorithms (e.g., [1-3]). Given HASARD's simplified dynamics and action space, it would need to introduce more complex tasks than those in Safety Gymnasium to stimulate the development of new algorithms. However, I did not observe any such complexity in the task design that would distinguish it from prior benchmarks.\n\n[1] CONSTRAINED POLICY OPTIMIZATION VIA BAYESIAN WORLD MODELS\n\n[2] Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation\n\n[3] SafeDreamer: Safe Reinforcement Learning with World Models", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9ixF7aYzIA", "reviewer": "Reviewer_NMGZ", "summary": "HASARD is a benchmark testing platform specifically designed for safe reinforcement learning, based on ViZDoom, providing a diverse range of 3D environments.\n\n1. The tasks on this platform require agents to pursue high rewards while considering safety strategies, moving beyond simple 2D navigation to incorporate complex elements such as spatial understanding.\n2. HASARD offers three difficulty levels and supports both soft and hard safety constraints, flexibly adapting to varying safety requirements.\n3. The platform integrates Sample-Factory, enabling high-speed simulation that allows agents to address real-world safety challenges while reducing computational costs.\n4. HASARD includes six environments based on ViZDoom and benchmarks various methods to demonstrate the limitations of existing technologies.", "questions": "1. The article does not provide an in-depth analysis of performance under different safety budgets. Is there a plan to supplement the experiments with varying safety thresholds to comprehensively demonstrate the trade-offs between reward and safety for each algorithm? This would be very helpful in understanding the adaptability of different methods under various safety requirements.\n2. Considering the limitations of ViZDoom in simulating real-world physics, have the authors explored other engines with superior physical simulation capabilities (e.g., Isaac Gym)?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The authors tested six baseline algorithms on HASARD and provided an analysis of the results.\n2. The tasks move beyond simple 2D navigation to incorporate complex elements such as spatial understanding", "weaknesses": "1. The reviewer believes that if the distinction between soft and hard constraints is merely based on whether the threshold is $0$, then other benchmarks share this characteristic, making this claim somewhat unsubstantiated.\n2. Although multiple methods were tested in the current experiments, there is a lack of analysis on performance under different safety budgets. It is recommended to include experiments with varying safety thresholds to better understand the trade-off between safety and reward for each algorithm.\n3. HASARD is based on the ViZDoom game engine, which, while computationally inexpensive, lacks detailed simulation of real-world physics.\n4. The anonymous video link provided by the authors is inaccessible.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XzfrpTkHPs", "reviewer": "Reviewer_Rd3K", "summary": "The paper proposes a new egocentric vision-based 3D simulated environment for benchmarking safe reinforcement learning. The benchmark is more realistic and challenging compared to common prior safe RL benchmark environments. In addition, the paper has evaluations for some safe RL algorithms on the proposed benchmark demonstrating its feasibility of use and the potential for building better approaches to perform more favorably on it.", "questions": "Please refer to the weaknesses above:\n\n- Unfortunately, while the paper is a decent attempt at building a safe RL benchmark, I am not convinced the safe RL community will be incentivized to use it. The main reason is that the notions of constraints in this benchmark are not directly tied to the very pragmatic safety considerations that need to be tackled in the real world - ranging from control systems to robotic deployments. Could the authors clarify how exactly they envision this benchmark to drive innovation in the safe RL community? And what sub-field of researchers would be likely to use it?\n\n- The benchmark feels a bit incremental compared to the already existing VizDoom framework that has been around for years. Can the authors clarify if the proposed modifications are non-trivial and if they can be broadly applied to potentially other frameworks like Minecraft and other games?\n\n- The evaluations are all with variants of PPO and no other safe RL algorithms are tested. It is unclear why this is the case, since in my understanding the benchmark should not be tied to a particular type of algorithm. Please clarify the evaluations and if there is any specific assumption on the type of safe RL algorithms that could be tested on the benchmark? \n\n- Can the authors make 1-1 comparisons with the proposed benchmark and the features of prior simulated and real world benchmarks that have been used by safe RL papers in  the past?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper is well motivated and targets an important problem - that of building realistic and reliable RL benchmarks, and more specifically benchmarks for safe RL. This involves addressing challenges with the simple natural of prior benchmarks - both visually and in terms of higher dimensional action space and increased temporal horizons. \n\n- The proposed benchmark HASARD is built on top of an existing game engine VizDoom and is able to inherit all of its properties for re-use. The multiple levels in HASARD can be potentially helpful in evaluating different notions of safety in proposed safe RL algorithms. \n\n- The paper has detailed evaluations of several safe RL algorithms on HASARD indicating that the framework is feasible for training constrained RL policies. The evaluations reveal that simple algorithms based on PPO and constrained PPO can achieve non-trivial performance in the benchmark and also reasonable constraint satisfaction. It is good to see that these simple algorithms do not saturate the benchmark and there is still a lot of room for improvement.", "weaknesses": "- Unfortunately, while the paper is a decent attempt at building a safe RL benchmark, I am not convinced the safe RL community will be incentivized to use it. The main reason is that the notions of constraints in this benchmark are not directly tied to the very pragmatic safety considerations that need to be tackled in the real world - ranging from control systems to robotic deployments. \n\n- The benchmark feels a bit incremental compared to the already existing VizDoom framework that has been around for years. The modifications for the different levels and environments in this framework do not capture the notions of open-world generalization and realism the field is headed towards in terms of evaluating RL systems. In addition, a lot of prior safe RL works have bechmakred their systems on real-world systems like robotic navigation and manipulation, and I am not convinced that a modified VizDoom framework is likely to create a reasonable impact in the community. \n\n- The evaluations are all with variants of PPO and no other safe RL algorithms are tested. It is unclear why this is the case, since in my understanding the benchmark should not be tied to a particular type of algorithm", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ao8TmYT9kX", "reviewer": "Reviewer_NtcD", "summary": "This paper introduces the HASARD, a benchmark designed for egocentric pixel-based safe RL in diverse and stochastic 3D environments. Unlike existing benchmarks, HASARD emphasizes spatial comprehension, short-term planning, and active prediction for high rewards while ensuring safety. It offers three difficulty levels, supporting both soft and hard safety constraints. The benchmark includes heatmaps for visual analysis, aiding in strategy development. By targeting vision-based embodied safe RL, HASARD addresses the need for benchmarks mirroring real-world complexities. The paper's contributions include the design of six novel ViZDoom environments with safety constraints, integration with Sample-Factory for rapid simulation and training. Evaluation of baseline methods within HASARD highlights challenges in balancing performance and safety under constraints.", "questions": "1. Is ViZDoom truly a 3D environment, considering its graphics appear pixelated and less detailed compared to modern 3D games? \n\n  \n\n2. Why are the baseline algorithms limited to PPO-based approaches? Could the paper include more diverse methods, such as model-based safe RL or constrained policy optimization (e.g., https://arxiv.org/abs/2210.07573)? \n\n  \n\n3. How can continuous safe RL algorithms be benchmarked when the paper only supports discrete action spaces?", "limitations": "", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper demonstrates several notable strengths across the dimensions of originality, quality, clarity, and significance: \n\n  \n\n1. **Originality**: It introduces HASARD, a benchmark specifically designed for vision-based embodied safe reinforcement learning (RL) in complex 3D environments. \n\n  \n\n2. **Quality**: Comprehensive design of 6 diverse environments with 3 difficulty levels each, offering a range of challenges. \n\n  \n\n3. **Clarity**: The paper is structured in a logical and coherent manner, facilitating the understanding of complex concepts. \n\n  \n\n4. **Significance**: The paper Addresses an important need in safe RL research for more realistic and challenging benchmarks. It enables systematic evaluation and comparison of safe RL algorithms in vision-based 3D settings.", "weaknesses": "While the paper makes valuable contributions, several areas could be improved: \n\n  \n\n1. The paper refers to ViZDoom as a 3D environment, but its pixelated, less detailed graphics compared to modern 3D games challenge this characterization. \n\n  \n\n2. **Narrow Range of Baselines**: Evaluations focus primarily on PPO-based algorithms. Incorporating approaches like model-based safe RL or constrained policy optimization (e.g., https://arxiv.org/abs/2210.07573) would enhance the assessment. \n\n  \n\n3. **Limited Visual Input Analysis**: Though vision-based learning is emphasized, the paper lacks analysis of how visual complexity influences performance. Exploring different visual conditions (lighting, distractors) and comparing raw pixels with simplified representations would highlight the unique challenges of vision-based safe RL, especially since the visual inputs in the environment appear less realistic. \n\n  \n\n4. **Action Space Limitation**: Only discrete action spaces are supported. It is unclear how continuous safe RL algorithms would be benchmarked. \n\n  \n\n5. **Real-World Relevance**: The connection between the benchmark tasks and real-world safe RL challenges needs clearer articulation. Providing examples of practical applications would strengthen motivation. \n\n   \n\nAddressing these points could strengthen the paper and increase the impact and utility of the HASARD benchmark for the safe RL research community.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)