Title: Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments
Venue: NeurIPS 2025
Accepted: yes
Keywords: Deep reinforcement learning

reinforcement learning

foraging

exploration

RNN analysis

open-ended learning

navigation

decision making

behavior analysis

cognitive science

neural dynamics

representations spatial memory

recurrent neural dynamics

joint neural-behavioral modeling
Abstract: Understanding the behavior of deep reinforcement learning (DRL) agents—particularly as task and agent sophistication increase—requires more than simple comparison of reward curves, yet standard methods for behavioral analysis remain underdeveloped in DRL.
We apply tools from neuroscience and ethology to study DRL agents in a novel, complex, partially observable environment, ForageWorld, designed to capture key aspects of real-world animal foraging—including sparse, depleting resource patches, predator threats, and spatially extended arenas.
We use this environment as a platform for applying joint behavioral and neural analysis to agents, revealing detailed, quantitatively grounded insights into agent strategies, memory, and planning.
Contrary to common assumptions, we find that model-free RNN-based DRL agents can exhibit structured, planning-like behavior purely through emergent dynamics—without requiring explicit memory modules or world models.
Our results show that studying DRL agents like animals—analyzing them with neuroethology-inspired tools that reveal structure in both behavior and neural dynamics—uncovers rich structure in their learning dynamics that would otherwise remain invisible.
We distill these tools into a general analysis framework linking core behavioral and representational features to diagnostic methods, which can be reused for a wide range of tasks and agents.
As agents grow more complex and autonomous, bridging neuroscience, cognitive science, and AI will be essential—not just for understanding their behavior, but for ensuring safe alignment and maximizing desirable behaviors that are hard to measure via reward.
We show how this can be done by drawing on lessons from how biological intelligence is studied.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper the authors propose 1) a methodology for Reinforcement Learning agent analysis that goes beyond the inspection of reward curves and uses techniques inspired from neuroscience and  ethology to lend insight to agent performance and capabilities and 2) then aim to show that these analyses tools can be applied to a simple RNN based RL agent demonstrating that such an agent possesses some abilities, such as planning, that go beyond what was previously assumed in model free agents.\n\nThe authors make the following contributions:\n\n * Introduce ForageWorld, an environment that simulates ecological systems  for the study of planning and memory agents\n * Demonstrate how to use tools from Neuroscience and ethology (animal behaviour) for analysis and interpretability of RL agents.\n * Show that RNN models, trained in a model free fashion, can learn to plan and use memory.\n * Release of agent and environment code as open source.\n\nThe paper focuses on a set of analysis methodologies that depend on observing jointly the agent's internal representations and behaviour: Goal Inference, Memory Span, Planning Horizon, Spatial Structure, Network Capacity, and Representations.  It is further claimed that RL agents often fail to generalise well in open-ended environments and that these methodologies will provide a clearer indication on which models possess the requisite skills for such environments. \n\nTo study the efficacy of these methods a recurrent network (RNN) based agent trained with PPO with an auxiliary loss on position prediction in the grid along with some methods for introducing sparsity in the network. The agent's hidden state can also be decoded to reveal allocentric positioning throughout acting.\n\nIt is reported that through the use of the analysis methodologies it is determined that the agent explores effectively using revisitation strategies, structures its objectives over the course of an episode utilising its memory, exhibits the emergence of abilities over training, uses its state to support memory and planning, and supports generalisation across environment treatments.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\nThe work is well motivated toward agent analysis providing a comprehensive breakdown of behavioural competencies (Table 2).  Understanding agent capabilities is critical as agents deploy into more sophisticated and real world settings.\n\nDraws inspiration from biology and neuroscience providing links into biological cognitive behavioural patterns and making use of existing and battle tested methodologies from those domains for understanding how neural activity is jointly represented with behaviour.\n\nThis approach highlights the importance of the interpretability of agents using methods that can gain insight into RL agents (or agents full stop). It is critical to understand agent capabilities especially as large models are increasingly deployed in the world. This paper presents a potential good step in the direction of contrasting and comparing such agents to understand which fundamental capabilities they have and will potentially provide more (or additional) insights than is done via agent evaluation benchmarks.\n\nThe authors demonstrate how a simple agent develops emergent planning and navigation strategies that are well adapted to the environment and generalise across episodes as the agent learns and retains information from its input for later use in an open-ended environment.\n\n**Weaknesses**\n\nI first want to clarify the main purpose of the paper.  It seems to me that the main takeaway is meant to be a novel suite of analysis methods inspired by behavioral and neural inspection methods from neuroscience etc.   There also seems to be a lot of emphasis on the capacity of the model free RNN based agent trained via PPO, is the primary purpose of this choice so as to showcase the efficacy of the analysis methods or is there some claim being made about this  specific agent architecture?  Further, there's some discussion about the sufficiency of neural analysis methods as applied to deep neural networks (as opposed to biological brains), it's unclear if this claim is addressed directly though the results in the paper, although the validity of the approach I think stands independently of this.  Overall, I think adding a bit more clarity about where its main contributions lie will be critical.\n\nI found the overall presentation to be somewhat disorganised throughout the paper. Figures are referenced out of order and many referenced figures are in the appendix. Also, the conclusions drawn in the results section could be better backed up with empirical evidence from the analyses methods used and more clearly linked to their biological counterparts as claimed.  For instance, when discussing revisitation strategies done by the agent and how it develops its objectives throughout an episode it seems clear that the agent has learned these capabilities successfully however, it isn't completely clear how the methods were applied to draw these conclusions. Would it be possible to reference the targets in table 2 and how they were applied as part of an overall methodology?  Further to this point, I believe that I'd have a hard time reproducing your results from what's been put down in the paper. It may be that the appendix adds some clarification here but this type of stuff should be accessible in the main body of the work.  If the methods are the real takeaway of this work then I believe that their use and novel character should be made clearer. \n\nThe ForageWorld environment appears to be a relatively simple toy environment.  Do these results generalise well to other environments?  Are there limits to these analysis methods that depend on this? It would be nice to see whether the thesis about the interpretability of planning and memory capabilities holds up under more complex dynamics.  A few more details about this environment in the main paper and what motivated its choice would be very helpful.  If possible, considering an additional, more natural environment could lend further weight to this work.\n  \nIt would be helpful to see a bit more justification around the model choice along with some additional detail around the architecture, training, RL algorithm and optimisation settings.  Further, some contrast to models that vary in some of these dimensions would be useful as a point of comparison especially from the perspective of the agent abilities that are discoverable through your analysis methods. Finally, like the environment, the choice of agent is somewhat limited compared to what's commonly used vis-a-vis large pre-trained models that can power generative agent based models. Considering such models would help ground the relevance of this work as well as provide a rich set of agent capabilities on which to perform this analysis.\n\nQUESTIONS:\nLine 28: I assume ML refers to \"Machine Learning\".  Referencing the term in full may add some clarity.\n\nDoes ForageWorld support multi-agent?\n\nIn the paper there is a lot of emphasis on agents trained via Deep RL methods but is this a necessary condition for the methods proposed?  Couldn't this be applied to agents optimised in different ways?  (e.g. behavioural cloning)\n\nIn the abstract, discussion, and possibly a few other times you indicate that there is a view that effective planning and memory use may require world models.  Is there some evidence that this view  is generally held? I was under the impression that model free agents can be fully capable of subsuming such capabilities.  Generative agent based models have certainly seemed to do so without any specific world modelling.\n\nWhy did you choose an RNN based agent?  Why not try feed forward as a baseline? What about a transformer based model?  Pre-trained models?\n\nIn the second paragraph of section 4.2 you refer to two figures (2 and 10), is this intentional?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces ForageWorld, a novel open-ended foraging environment inspired by real-world animal behaviors (e.g. depleting resources, predators, partial observability). The authors trained RNN-based, model-free DRL agents (PPO + GRU) to survive in this environment, and after which applied a behavior-first analysis toolkit adapted from neuroscience, cognitive science and ethology and find that trained model-free DRL agents exhibit structured foraging strategies with distinct phases and displays planning-like behavior without explicit planning modules or world models. The authors argue that by conducting joint behavioral and neural analysis for DRL agents, similar to those for animals, yields deeper mechanistic insight into how complex decision-making tasks are solved. Overall, this paper bridges ML and neuroscience by improving interpretability and evaluation of deep RL agents in complex environments with neuroscience insights.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. This paper adapted a behavior-first toolkit from neuroscience, cognitive science, and ethology (GLMs, phase segmentation, and linear state decoding) on the trained RNN-based DRL agents. Their analyses expose clear strategy phases (exploration → targeted patch revisitation) and show that allocentric position is linearly decodable up to roughly 50–100 steps into both past and future, demonstrating that planning-like computation can arise from purely model-free RNN agents alone. \n2. Results of this paper draw a connection between DRL agent and animal behavior: For example, in Sec. 4.1, it states that agent's behavior phase shifts are analogous to animal foraging behavior phase shifts, and that their early trajectories resemble the “azimuthally rotating, outward spiraling loops” seen in ants and bees. In Fig. 3, it shows patch-choice GLMs rely on multiple variables such as proximity, predator history and uncertainty, which are commonly used to model rodent foraging decisions. By mapping agent strategies onto these established animal behavioral patterns, the work creates a concrete connection between DRL agent evaluation and behavior neuroscience, which may inspire future neuroAI work and interpretable RL evaluation.\n3. This paper presents ForageWorld, a novel open-ended naturalistic foraging environment built based on Craftax by adding patchy renewable resources, dynamic predators and a survival-oriented reward function. This simulated environment provides a valuable tool for running complex, naturalistic behavioral experiments under realistic ecological constraints and cognitive demands. It can also serve as a platform for testing brain analysis techniques as the current DRL agents are likely simpler than biological agents. \n\n**Weaknesses**\n1. The discussion on existing model-free RNN-based DRL agent work is limited. As the authors claimed, one of the key contributions is showing “model-free RNN-based DRL agents can exhibit structured, planning-like behavior purely through emergent dynamics without requiring explicit memory modules or world models”. Although the authors briefly mentioned several earlier related literatures in Line 269-270, it did not discuss [1], a more recent paper that already claims to provide causal evidence of emergent planning in model-free RL agents. A detailed discussion will help clarify this paper’s novel contribution on this topic.\n\n2. Current neural analysis is limited as it focuses on linear decoders of the GRU hidden state. One possible next step could be incorporating methods such as dynamical-systems analysis or representational-similarity analysis to uncover low-dimensional structure in the hidden activity. Those methods, which are widely used in neuroscience, could offer deeper insights into the trained GRU network. \n\n[1] Bush et al. Interpreting Emergent Planning in Model-Free Reinforcement Learning. ICLR 2025.\n\nQUESTIONS:\n1. As mentioned in Weaknesses 1, a recent work [1] causally shows mechanistic evidence that model-free RL agents can exhibit planning-like behaviors. Could you clarify and summarize how this paper advances beyond [1]?\n2. In Sec. 6.1 of [1], the authors inject learned “plan-concept” vectors to the agent’s activations and observe the causal effect of steering the agent from a short to a long route. Do you think there could be an analogous intervention, such as adding a “visit-patch-X” or “evade-predator” vector, that could demonstrate causal control over your agent’s planning-like behavior?\n3. Can you provide several examples of candidate neuroscience tools, aside from those used in this paper’s experiments, that could be applied to evaluate DRL agents in ForageWorld?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors of the paper \"Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments\" investigates how model-free reinforcement learning agents internally develop planning-like behaviors. To this end, the authors introduce an environment ForageWorld, which serves as an open-ended task inspired by animal foraging. Based on this the authors investigate properties of the learned GRU policy.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n* ForageWorld exhibits rich dynamics with ecological motifs, which overall provides a nice testing ground.\n* The efforts of this paper are well motivated and follow an interdisciplinary approach.\n* It is notable to find that models such as GRUs that are trained model-free to exhibit planning-like behavior.\n\nWeaknesses:\n\n* The authors study only a single type of agent, which is a GRU trained with PPO, which does not speak for the broad applicability of the methods employed in this work. In particular, one would have hoped that the authors study also LLM-based agents (without or with fine-tuning) on such a task. This would not only strengthen the broader applicability point, but also serve to make the work more visible.\n* There is little discussion or evidence surrounding this environment's advantage as compared to other open-ended environments such as Minecraft (which is mentioned). Could one not perform the same analysis on such environments?\n\nQUESTIONS:\n* What environment features contribute to the emerging behaviors? I believe this study would benefit from ablations on the environment itself.\n* Can the same findings also be obtained for pre-trained LLMs, since those are generally found to plan several tokens ahead too?\n\nLIMITATIONS:\n* The study focuses on GRUs trained with PPO only. This is definitely a limitation as it does not support the broad applicability of the analysis pipeline.\n* Unless otherwise convinced, I believe that environments such as Minecraft already capture many aspects of the proposed ForageWorld.\n* It is unclear which environmental aspects cause specific behaviors in the agent", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces “ForageWorld”, a procedurally generated open-ended reinforcement learning environment with ecological structure  (built on an existing framework called Craftax). The environment replicates ecological pressures through partial observability, diminishing food sources, shifting predator threats, and biological drives like hunger and fatigue. They conduct joint behavioral and neural analysis using tools adapted from neuroscience, cognitive science and ethology to study reinforcement learning agents trained in ForageWorld environment (using path analysis, generalized linear models, and recurrent state decoding). The presented work demonstrates that analyzing models this way reveals subtle learning strategies and internal representations that standard performance measures often overlook, and shows connections between artificial and biological systems. \n\nThe authors train PPO agents across five configurations (predator blind agent - agent that cannot cause damage to the predators, normal field of view - aerial view of shape 9x11 grid around the agent with the agent in the center, front-facing field of view - an aerial view of the grid rows in-front of the agent instead of 9x11 grid with agent the center as input, auxiliary path integration objective - auxiliary loss function for linear decoding of the position in reference to the start position as the origin from the recurrent state) - \n\n1. Baseline RNN agent (non predator-blind agent with normal field of view and auxiliary path integration objective)\n2. Predator-blind RNN agent\n3. RNN agent without auxiliary path integration objective\n4. RNN agent with front facing field of view\n5. Memoryless agent (feed-forward neural network instead of RNNs)\n\n- The authors highlight the emergence of structured phase like foraging behavior analogous to those observed in animals. Similar to rodents, after initial exploration of the environment, the models show goal-directed revisitation of previously discovered states. Additionally, the authors suggest that the initial exploration qualitatively resembles insect search behavior and show that agents integrate information in a multi-objective manner while making revisitation decisions.\n- In recurrent state decoding analysis the authors find that the past and future allocentric positions (position with respect to the origin/start-state) can be decoded from the recurrent state of the agent better than the egocentric position (position with respect to the agent’s current position), and that the path integration objective is necessary for decodable spatial representations. In addition, decoding accuracy improved over training and removing path integration loss reduced performance on large arenas due to agent’s inability to predict its own position.\n- Pruning for sparse activations improved allocentric decoding in RNN agents.\n- Memoryless agents show reduced survival and poor coverage of the arena in comparison to the RNN agents. They do not exhibit emergent phase like foraging behavior and get trapped in local loops.\n- Both predator-blind agents and non predator-blind agents achieve similar survival performance and converge to similar movement strategies favoring predator avoidance instead of combat.\n- Agents with front-facing field of view performed more dense local exploration in early exploration period, and transitioned faster to longer range exploration compared to normal field of view.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths -\n \nI would like to thank the authors for submitting their interesting work.\n\n- Their work introduces a new benchmark with ecological constraints useful for studying emergent phenomena in deep RL agents which could be of interest across multiple disciplines.\n- The emergent agent behavior observed in this work and its similarities to animal behavior is very interesting.\n- The experimental analysis clearly isolates and demonstrates the emergent phenomena discussed by the authors.\n- The comprehensive evaluation across multiple agent configurations strengthens the validity of the conclusions.\n\n\n\n### Weaknesses -\n\n- While agent behavior in reinforcement learning is significantly influenced by the reward function, the paper does not investigate how variations in reward design affect emergent behaviors.\n- The experiment for agents with front-facing field of view seems incomplete (Figure 15) as it contains only early-to-mid training data.\n- A more comprehensive review of related work on emergent cognitive behaviors and interpretability in reinforcement learning would strengthen the paper's positioning.\n- A more detailed explanation of the metrics in Figure 4 would enhance the paper's clarity.\n\nQUESTIONS:\n- It is unclear how path 1, 2, 3 and 4 were classified in Figure 1, 6, 10 and 14. Do they belong to different episodes or the same episode? If they belong to the same episode, what was the criteria of differentiation?\n- How does survival time evolve across episodes during the training process?\n- What dependencies or correlations exist between survival time and other experimental metrics throughout training?\n- In Figure 4 and similar figures, the 'distance traveled' subplot contains 'Early in Episode' and 'Late in Episode' categories, but it is unclear how these categories were defined. Additionally, the predator related metrics could be better explained. The subplot uses the title “pred. kill” whereas the text mentions “predator defeat”. Is it possible to provide more comprehensive definitions for the metrics used in this figure?\n- The caption for Figure 11 states that \"The absence of structured spatial uncertainty plots (top left) reflects degraded internal spatial representation,\" but this is unclear. How does the absence of the plot indicate degraded spatial representation?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}