Title: Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems
Venue: NeurIPS 2024
Accepted: yes
Keywords: Multi-agent system

Multi-agent reinforcement learning

Transfer learning

Human agent interaction

Scalability
Abstract: Due to the exponential growth of agent interactions and the curse of dimensionality, learning efficient coordination from scratch is inherently challenging in large-scale multi-agent systems. While agents' learning is data-driven, sampling from millions of steps, human learning processes are quite different. Inspired by the concept of Human-on-the-Loop and the daily human hierarchical control, we propose a novel knowledge-guided multi-agent reinforcement learning framework (hhk-MARL), which combines human abstract knowledge with hierarchical reinforcement learning to address the learning difficulties among a large number of agents. In this work, fuzzy logic is applied to represent human suboptimal knowledge, and agents are allowed to freely decide how to leverage the proposed prior knowledge. Additionally, a graph-based group controller is built to enhance agent coordination. The proposed framework is end-to-end and compatible with various existing algorithms. We conduct experiments in challenging domains of the StarCraft Multi-agent Challenge combined with three famous algorithms: IQL, QMIX, and Qatten. The results show that our approach can greatly accelerate the training process and improve the final performance, even based on low-performance human prior knowledge.

Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems

Abstract

Due to the exponential growth of agent interactions and the curse of dimensionality, learning efficient coordination from scratch is inherently challenging in large-scale multi-agent systems. While agents' learning is data-driven, sampling from millions of steps, human learning processes are quite different. Inspired by the concept of Human-on-the-Loop and the daily human hierarchical control, we propose a novel knowledge-guided multi-agent reinforcement learning framework (hhk-MARL), which combines human abstract knowledge with hierarchical reinforcement learning to address the learning difficulties among a large number of agents. In this work, fuzzy logic is applied to represent human suboptimal knowledge, and agents are allowed to freely decide how to leverage the proposed prior knowledge. Additionally, a graph-based group controller is built to enhance agent coordination. The proposed framework is end-to-end and compatible with various existing algorithms. We conduct experiments in challenging domains of the StarCraft Multi-agent Challenge combined with three famous algorithms: IQL, QMIX, and Qatten. The results show that our approach can greatly accelerate the training process and improve the final performance, even based on low-performance human prior knowledge.

1 Introduction

As an essential attribute of multi-agent reinforcement learning (MARL), scalability deserves more attention since it contributes to autonomous collective learning behaviors among agents. In many engineering and scientific disciplines, algorithms must possess sufficient scalability to cooperate properly [1, 2, 3]. Unfortunately, training a large number of agents presents inherent challenges. The joint action-state space increases exponentially with the number of agents, resulting in the curse of dimensionality [4, 5]. Agents, suffering from sparse rewards and sample inefficiency [6, 7], encounter 'start-up' problems [8] and often get trapped in local optima, to the extent that learning becomes impossible in the worst cases [9, 10].

To alleviate the exploration burden and overcome the curse of dimensionality, knowledge transfer methods have attracted significant research interest in dealing with large-scale multi-agent systems (MAS) [11, 12, 13]. As the most intuitive and common source, transferring prior knowledge from humans has received considerable attention [14, 15]. An important line of research involves extracting prior knowledge from expert trajectories through imitation learning to address sequential decisionmaking problems [16, 17]. However, despite the emphasis on Human-on-the-Loop [18], most research still focuses on step-by-step action demonstrations, which require high-quality and comprehensive prior knowledge [11, 14, 17]. The deployment of this type of human knowledge becomes increasingly

difficult in complex tasks, necessitating overwhelming human effort. Additionally, creating humanguided state-action pairs for each scenario is so time-consuming that even human experts struggle to anticipate actions in these highly challenging tasks.

Fortunately, human guidance is not limited to step-by-step action demonstrations, and other high-level knowledge exists to reduce the human effort involved [14]. Human prior knowledge can be abstracted and transferred to agents through abstract rules or heuristics [19, 20, 21]. Although the provided knowledge is suboptimal, it can greatly reduce the computational burden and improve algorithm performance. However, current discussions are still limited to single- or two-player scenarios. Unfortunately, utilizing human prior knowledge properly is significantly more challenging in MAS. Due to non-stationarity and multiple Nash equilibria, most single-agent methods are unsuitable for multi-agent scenarios. Additionally, human knowledge is usually applied indiscriminately, and mapping knowledge from humans to agents is still in its early stages [15].

To better transfer knowledge from humans, it's essential to understand the nature of human knowledge. Despite accomplishing many complex tasks in daily life, human activities do not require much active attention (Figure 1). When deciding to get up and walk, the brain uses the prefrontal cortex to generate commands, but the intricate coordination of sensory inputs and muscles that follows does not require any conscious attention. Instead, it's mostly executed by a lower nerve network in the spinal cord, sometimes called central pattern generators (CPG) [22]. Indeed, humans are naturally adept at abstracting and providing high-level knowledge, and

asking them to provide detailed step-by-step action guides is overly demanding. It may be preferable to have humans deliver abstract knowledge at the top level, while agents spontaneously decide on the utilization of the proposed knowledge based on their 'CPG'.

Inspired by the hierarchical control of human daily activity, we propose a novel knowledge-guided MARL framework to integrate abstract human knowledge into MARL algorithms in an end-to-end manner. As several works have demonstrated, a hierarchical structure [23] and graphical models [1] can greatly benefit large-scale MAS. We believe our approach, the hierarchical human knowledge multi-agent reinforcement learning framework (hhk-MARL), can alleviate learning difficulties among a large number of agents. In our framework, trainable fuzzy logic rules are applied to represent human knowledge. Since the prior knowledge is suboptimal, our framework allows agents to adjust the utilization of proposed knowledge through hyper-network based knowledge integration. Additionally, to enhance agent coordination, we construct a graph to determine the importance of agents and simplify their relationships. By applying human knowledge at the top level while maintaining agents' ability to develop self-policies at the bottom level, our hierarchical method helps 'warm-start' the training process and overcomes the learning difficulties of large-scale MAS. Experimental results from challenging tasks in the StarCraft Multi-agent Challenge (SMAC) [24] demonstrate that our approach can be easily combined with various MARL algorithms, significantly improving their learning efficiency.

2 Preliminaries

2.1 Partially observable Markov game

We view our problem as a cooperative multi-agent task. This task can be modeled as a decentralized partially observable Markov decision process (Dec-POMDP) [25], given by a tuple G :

where s ∈ S defines the global environment state. The observations of each agent i ∈ N ≡ { 1 , . . . , n } are partially observable o i ∈ O i , which are determined by the observation function Z ( s, i ) : S × N → p ( O ) . Based on its local observation o i , each agent i selects its action u i ∈ U i at each time step according to its stochastic policy π i : O i × U i → [0 , 1] . Then, based on the state transition function P : S × U 1 ×···× U n → S , the joint action of all agents ⃗ u ∈ ⃗ U changes the environment into a new state. All agents share the same reward function r ( s, ⃗ u ) : S × ⃗ U → R , and aim to learn poli-

cies to maximize action-value functions Q ( s t , ⃗ u t ) = E ⃗ u t +1 ∼ ⃗ π,s t +1 ∼ P [ ∑ Th -t l =0 γ l r t + l ( s t , ⃗ u t ) | s t , ⃗ u t ] where γ is the discount factor and Th is the time horizon.

2.2 Fuzzy logic

Since human knowledge is highly abstract and uncertain, it is inappropriate to represent knowledge with hard rules [26]. On the contrary, fuzzy logic can tackle issues of uncertainty and lexical vagueness, pacing its way to depict human imprecise knowledge [20]. A fuzzy logic rule is usually in the form of 'IF X is A and Y is B THEN Z is C ' with a membership function µ for each fuzzy set used to calculate the truth value T of each precondition:

where x 0 and y 0 are the observation values for X and Y , respectively. To derive the conclusion of this fuzzy rule, both preconditions must be satisfied:

Finally, the conclusion's strength, ω , is obtained as follows:

Therefore, a fuzzy logic rule takes the observation values as inputs and outputs the value of the conclusion to illustrate how likely it is to operate the designed actions under the current observations.

2.3 Knowledge representation and integration

Although human knowledge is highly instructive, such prior knowledge is suboptimal and covers only a small part of the state space. Applying it indiscriminately can hinder the training process. To avoid negative knowledge transfer, agents should have the freedom to decide the utilization of transferred knowledge. For better intuition, a teacher (human) and student (agent) model could be considered. To guide student research, the mentor might give the doctoral student some comments. For example, a comment for literature review could be: 'Read paper with high citation score' , which can be expressed in the fuzzy logic rule as 'IF O is high , THEN action is read '. Here, O is the observation of the citation score, and high is a fuzzy set M whose membership function could simply be, µ high ( o ) : clip [0 . 05 o, 0 , 1] . Therefore, the higher the score, the stronger the action read will be. Apparently, such knowledge is abstract and proposed for a specific state in the research process. Indeed, it is unrealistic to require the mentor to design a comprehensive checklist, and uncertainty and lexical vagueness are inevitable. The student is expected to autonomously decide when to follow the mentor's advice and when to trust their own judgment. Inspired by the CPG of human hierarchical control (Figure 1), to integrate abstract human knowledge into the agent learning process, we design a knowledge 'CPG' for the agent to freely decide how to leverage the suboptimal knowledge.

3 Human knowledge guided hierarchical framework

In this section, we will illustrate a novel human knowledge-guided MARL hierarchical framework (hhk-MARL) that integrates abstract human knowledge into MARL in an end-to-end manner to enhance agents' learning efficiency. The overall architecture is shown in Figure 2, which is divided into three levels, mimicking the human hierarchical control (Figure 1). Inspired by the CPG of humans, a hyper-network based knowledge integration is built to allow agents elegantly leverage the proposed human prior knowledge. The details of each module will be elaborated in the following sections, and the meanings of symbols can be found in Appendix A.3.

3.1 Knowledge controller

Since requiring humans to provide comprehensive step-by-step demonstrations for large-scale MAS is unrealistic, we focus on abstract and suboptimal human knowledge to reduce the human effort involved. As introduced in Section 2.2, fuzzy logic is applied to capture human imprecise knowledge. Compared to other knowledge representation methods, fuzzy logic is closer to the structure of human knowledge, making it more interpretable. Furthermore, it has been proven that fuzzy logic is more suitable for training large-scale multi-agent systems with the advantage of generalization

The Top Level

Group controller

Knowledge integration i

Knowledge controller

Group controller

Group 1

01

(Graph)

M1.2

{QF,QLoc]

θ

→Qi

controller 1

Knowledge

controller N

Knowledge

M

Ha.,Aa)

{比}←

入1,A

入1,5

入3,6

入4,5

GroupP

0

QN

a

a

{}

{QF}

Qtot.

Knowledge integration

Mixing network

Knowledge controller i

Fuzzy rule 1

Qloc(t',)

Fuzzy rule 2

MLP

Integration 1

Integration 2

Integration N

ht-1

GRU

T

Qioc

QLoC

QLOC

Fuzzy rule L

{QLOc}

MLP

QLOC(tN,)

Qtoc(t1,)

以

PL

.

(ot,ut-1)

Agent N

Agent 1

MIN

PLz

Local Q network of agent i

(o,ut-1)

The Bottom Level

(ot,ut-1)

[27]. Inspired by previous works on knowledge representation with fuzzy logic [20, 27], we leverage fuzzy logic to abstract human prior knowledge in this work. The general form of each rule can be represented as:

Rule L : IF O 1 is M L 1 AND O 2 is M L 2 AND . . . AND O z is M L z , THEN Action is u kk

Here, O i z are variables about the system extracted from agent i 's observations, and M i L z are fuzzy sets for the corresponding variables O i z . The strength of Rule L on corresponding actions is obtained:

where o i z are observation values for O i z and µ i L are membership functions for the fuzzy sets M i L . An example of fuzzy logic rule design is illustrated in Section 2.3 for clarity. For fairness, humans are only allowed to propose guidance based on agent's local observations.

As the transferred human knowledge is very rough, inspired by [20], we add trainable weights β to adapt proposed knowledge to current tasks. With these trainable weights, the knowledge can be optimized similarly to a neural network. For each rule, there are z +1 corresponding weights: the first z weights adjust the causal degree of each precondition, and the last weight β z +1 indicates the confidence in the proposed knowledge. Therefore, the human preference vector Q F L for fuzzy rule L on each action can be represented as (an example of human preference vector is in Appendix A.6):

These trainable weights are initialized at 1 to avoid disturbing the prior knowledge, and then adjusted through the reinforcement learning based on the reward signal. However, it is worth mentioning that a fuzzy rule can be initialized with a higher weight when there is high confidence in it.

We refer to this knowledge representation module as the knowledge controller, shown in Figure 2 bottom right. To maintain the merit of scalability, this knowledge controller is shared among all agents. Specifically, for agent i , the knowledge controller takes the agent i 's observation o i as input

Algorithm 1 Human knowledge guided hierarchical MARL

Input: MARL algorithm, Knowledge controller, Group controller, Knowledge integration Output: Human knowledge guided MARL model

1: Initialize the parameters of MARL algorithm, knowledge controller, group controller, knowledge integration

2: for episode = 1 to max-episode do

3: for t = 1 to max-episode-length do

4: for agent i = 1 to N do

5:

Calculate

Q

i

LOC 6: Use fuzzy rules for Q i F = { Q i F 1 , . . . , Q i F L }

based on MARL algorithm

7: Input { Q i LOC , Q i F } into knowledge integration for knowledge guided vector Q i

8: Sample action u i from Q i with ϵ -greedy policy

9: end for

10: Execute ( u 1 , . . . , u N ) to obtain reward r and new observation o ′ from environment

11: Store ( o, u, r, o ′ ) in replay buffer D , and set o ← o ′

12: Sample a random minibatch of G samples ( o g , u g , r g , o ′ g )

13: Obtain { λ 1 , . . . , λ N } , set Q tot , and update the Q-network by minimizing the loss: L tot 14: end for

15: end for

and outputs L number of human preference vectors based on built-in fuzzy logic rules:

3.2 Knowledge integration

Although trainable weights are added to the knowledge controller to mitigate the knowledge mismatch problem, the proposed knowledge is still quite rough, covering only a small portion of the state space. Since humans and agents have different perceptions and knowledge structures, it is more appropriate to allow agents to determine the utilization of prior knowledge. To not distort human knowledge and avoid negative knowledge transfer, we propose a hyper-networks based knowledge integration to integrate human prior knowledge into agents' policies. Similar to human CPG (Figure 1), this approach allows humans to design abstract knowledge from a high-level, while agents can autonomously decide whether to accept the proposed knowledge and how to leverage it.

Although applying a concatenated neural network as the knowledge integration is straightforward, it is difficult to capture the dynamic knowledge requirements in different states. To allow agents to automatically adapt to human guidance, motivated by previous research [20], we propose a hypernetworks based knowledge integration that allows agents to refine the proposed prior knowledge based on the local observation. As shown in Figure 2 (top left), the knowledge integration consists of two networks that take the human preference vectors Q i F and the agent preference vector Q i LOC as input, and output the knowledge-guided action preference vector Q i based on the agent i 's local observation. Formally, the first network takes the observation o i of agent i as input and generates weights for the second network, which combines the agent's policy with human knowledge:

Here, h α ( · ) is the hyper-network that generates the weights θ for the integration k θ ( · ) . To encourage agents to frequently explore human knowledge at the start, a hyperparameter Ω is considered:

This hyperparameter is initialized to 1 and quickly decreases to zero to not impair the agents' autonomy in knowledge adjustment. Similar to the knowledge controller, the knowledge integration is also trained based on the reinforcement learning process and this module is also shared among all agents.

where:

3.3 Group controller

Besides the 'start-up' problem from the curse of dimensionality, another difficulty in large-scale MAS is the intricate interactions among agents, which intensify as the number of agents increases. To enhance agent coordination and further improve scalability, motivated by the graph's ability to simplify relationships [1, 28], a simple neural network is applied to generate a cooperation graph among agents (Figure 2 top right). This group controller uses local observation to output cooperation tendency among agents. Specifically, based on o i , agent i can propose the allies it wants to cooperate with, following the tendency strength λ i,j :

where λ i,j is the cooperation tendency of agent i toward agent j . After agents deliver their tendencies, this will form a relationship graph (an example is shown in Figure 9):

Here, an agent's importance is considered based on other allies' cooperation tendencies toward it:

where λ i is the importance weight of agent i and will be used to weight the agent's selected action. The group controller is also shared among all agents. Additionally, to not violate the principle of centralized training with decentralized execution (CTDE), this module will only be applied during the training process.

3.4 Knowledge guided hierarchical MARL

To transfer knowledge from humans, we integrate abstract human knowledge into the agent learning process by mimicking the hierarchical control of human daily activities. As demonstrated in Figure 2, human can easily propose abstract knowledge at a high-level, while agents autonomously form specific action-state strategies based on the MARL algorithm. This learning framework is end-to-end and can be combined with various MARL algorithms. To clarify this process, QMIX [29] algorithm is applied as an example here. Initially, the agent i 's preference vector Q i LOC ( τ i , · ) is calculated based on its local Q network. Then, the human preference vectors Q i F ( o i ) are generated from the knowledge controller, mentioned in Section 3.1. Next, following Equation 8, the human knowledge is integrated into agent i 's policy, forming the knowledge-guided action preference vector Q i . This vector Q i is used to sample the action u i of agent i based on the ϵ -policy with the Q value Q i ( o i , u i ) . To imply the importance of agents in the group, the importance weight λ i is obtained from Equation 13 to weight Q i ( o i , u i ) and form the weighted Q value Q i ( o i , u i ) . Finally, the weighted Q values from all agents are aggregated to derive the global value Q tot using the QMIX mixing network:

Subsequently, Q tot is trained to minimize:

where y t is calculated as follows, and r t is the reward at time step t :

To comply with the CTDE, the group controller and mixing network are only applied during the training process, while the local Q network, knowledge controller and knowledge integration are applied at both stages. The pseudo-code of our method is provided in Algorithm 1.

4 Experiments and analysis

In our experiments, we aim to answer the following questions: (1) Can the proposed framework improve the scalability of MARL algorithms? (2) What's the function of each module in our framework? (3) Is using suboptimal human knowledge justified, and how does its quality influence the learning process? To answer these questions, we conduct our experiments on challenging scenarios of the StarCraft Multi-Agent Challenge (SMAC) [24] with an increasing number of agents involved.

4.1 Experimental setting

(a) 5m Vs 6m

(b) 10m Vs 11m

(c) 18m Vs 20m

(d) 27m Vs 30m

(e) 35m Vs 40m

5m_Vs_6m

10m_Vs_11m

18m_Vs_20m

100

100

100

IQL

IQL

IQL

TestWinRate

%

80

hhkQMIX

MedianTestWinRate

%

80

hhkQMIX

A

Median Test Win Rate

%

80

hhkQMIX

W

60

hhkIQL

60

hhkIQL

60

hhkIQL

Median

40

40

40

20

20

20

0

0.25

0.50

1.50

1.75

2.00

0

0.25

0.50

0.751.001.251.501.752.00

0

0.25

0.50

1.50

1.75

2.00

Steps (mil)

Steps (mil)

Steps (mil)

27m_Vs_30m

100

IQL

%

80

Median Test Win Rate

hhkQMIX

60

hhkIQL

WW

40

Mm

20

0

0.25

0.50

1.50

1.75

2.00

Steps (mil)

35m_Vs_40m

100

IQL

%

80

MedianTestWinRate

hhkQMIX

60

hhkIQL

40

20

Q

0.25

0.50

0.751.001.251.501.752.00

Steps (mil)

Focusing on micromanagement control, SMAC has been proposed as a common benchmark for MARL methods. In the following experiments, we test our framework on challenging scenarios in SMAC from the 'Hard' and 'Super Hard' categories, setting the game AI difficulty to 'Very Hard'. To demonstrate the impact of the agent number on algorithm performance, we add two self-designed scenarios with more agents involved. To verify the effect of our end-to-end framework, we combine our method with three famous MARL algorithms: IQL [30], QMIX [29], and Qatten [31], naming the corresponding approaches hhkIQL, hhkQMIX, and hhkQatten, respectively. In this study, each algorithm and its corresponding approach are configured with the same hyperparameters and neural

Memebership function for enemyDistance is small

0.8

0:6

0:4

0-2

(0.6667,0.1)

02

04

0.6

0.8

network structures. Furthermore, all experimental results are derived across three separate trials with different random seeds, with 32 test episodes in each trial. The shaded region in Figure 4 and 7, and the error bar in Figure 5 denote standard deviation in this work. Details of the hyperparameter settings are in Appendix A.5.

Instead of requiring high-quality expert demonstrations, this work aims to transfer suboptimal knowledge from humans, considering eight pieces of knowledge for challenging tasks in SMAC. Due to space limitations, we use the human prior knowledge: 'Attack the closest enemy' as an example. As illustrated in Section 3.1, this abstract knowledge can be represented with a fuzzy logic rule:

· IF e _ d is small , THEN action is attackEnemyId .

Here, e _ d represents the agent's observation of the distance between itself and enemies. The corresponding membership function for the fuzzy set small is elaborated in Figure 3 and is defined as a simple linear function. More details about the applied human suboptimal knowledge can be found in Appendix A.6. As demonstrated, these fuzzy logic rules are abstract, suboptimal, and designed for specific states (e.g., attack actions are not always available), resulting in a win rate of 0 % when

agents are solely manipulated by the proposed knowledge. Nonetheless, these rules are interpretable and easy to design and understand from the human aspect. All rules are designed based on the agent's local observation. Furthermore, it is worth mentioning that the proposed human prior knowledge is not well-crafted and is highly subjective, with no strict requirement for its appropriateness. Anyone can develop their fuzzy logic rules based on their own experience.

4.2 Result and evaluation

To answer the first question, we deploy the approaches in scenarios with an increasing number of agents. The learning curves for all approaches across all tasks are illustrated in Figure 4, and the performance comparison based on the number of agents is elaborated in Figure 5. In conclusion, the experimental results indicate that even based on highly abstract and low-performance human prior knowledge, our knowledge-guided approaches not only significantly accelerate the training process but also enhance the final performance across scenarios with various numbers of agents.

As the number of agents increases, the joint action-state space expands exponentially, making it exceedingly challenging for agents to learn from scratch. Despite these challenges, our approaches still manage to overcome the 'start-up' issue (Figure 4). By alleviating learning difficulties, while baseline algorithms fail in tasks with many agents (IQL in Figure 4(d), IQL and QMIX in Figure 4(e)), our approaches help agents overcome the initial learning challenges, improving scalability. Furthermore, as demonstrated by the win rate curves in Figure 4(a), our approaches greatly accelerate the training process, benefiting the initial stages of agent learning. Notably, our method (hhkIQL), even combined with the least effective baseline algorithm (IQL), achieves performance comparable to the best baseline algorithm (QMIX).

To better visualize our method in scenarios with different numbers of agents, we summarize the algorithms' performance in Figure 5. By leveraging human prior knowledge, our end-to-end method consistently helps MARL algorithms improve their performance across various numbers of agents. Additionally, it is worth mentioning the improvement our method brings to the IQL algorithm, shown in the first image of Figure 5. As one of the oldest fully decentralized MARL algorithms, IQL inherently struggles to handle coordination in large-scale MAS, displaying low performance in

100

IQL Vs hhkIQL

100

100

QattenVshhkQatten

IQL

hhkIQL

hhkQMIX

Median Test Win Rate°

%

80

80

80

60

60

60

40

40

40

20

20

20

0

5

10

18

27

35

0

5

10

18

27

35

0

5

10

18

27

35

The number of agents

all scenarios. By contrast, benefiting from the integration of human knowledge and the relationship graph to mitigate 'start-up' issues and enhance cooperation, our approach significantly improves the scalability of the IQL algorithm.

4.3 Ablation study

To answer the following two questions, we design two ablation experiments: one to identify the function of each module and another to assess the influence of human suboptimal knowledge. Considering our improvement on the IQL algorithm, we use it as the basis in the '10m vs 11m' scenario to provide a clear comparison. The ablation results are detailed in Figure 7.

4.3.1 Module function

The experiment results on module functions are depicted in Figure 7(a), where 'hhkIQL-graphOnly' represents our method with only the group controller, 'hhkIQL-humanOnly' indicates our method integrating solely human knowledge, and 'hhkIQL-fixedKnow' denotes our method without trainable parameters in fuzzy logic rules.

(a) Effect of different modules

(b) Effect of human knowledge applied

Ablation study (10m_Vs_11m)

Ablation study (10m_Vs_11m)

100

100

IQL

IQL

hhkIQL

hhklQL (1 rule)

%

MedianTest WinRate

80

hhklQL-humanOnly

hhklQL-graphOnly

Median Test Win Rate

%

80

hhkIQL(5 rules)

hhklQL (3 rules)

hhkIQL-fixedKnow

hhkIQL (8 rules)

hhklQL(random)

60

60

40

40

20

20

0

0.25

0.50

1.50

1.75

2.00

0

0.25

0.50

0.751.00

1.25

1.50

1.752.00

Steps (mil)

Steps (mil)

Effect of group controller: To elaborate on the role of the group controller, we first present agents' cooperation graph in Figure 6. The relationship graph reveals that the cooperation demand changes over time. With the group controller, agents can independently choose allies to collaborate with, and important agents are emphasized. As depicted in Figure 7(a), the group controller enhances cooperation among agents and accelerates the training process, evidenced by a higher convergence rate compared to that of the IQL algorithm.

Effect of human knowledge: Although the transferred knowledge is suboptimal, human prior knowledge can significantly improve per-

= 31

0.11

51/5

t = 27

formance. The knowledge integration, functioning as a 'CPG', allows human to effortlessly provide abstract knowledge from a high-level perspective. As a result in Figure 7(a), the knowledge-guided algorithm achieves overall better performance than the baseline algorithm. However, it is worth noting that this ablation algorithm can be further improved with the group controller installed, as evidenced by comparing it to the hhkIQL approach.

Trainable knowledge: The benefits of trainable knowledge are demonstrated in Figure 7(a). Although both approaches achieve similar performance, the approach with trainable fuzzy logic rules realizes a faster convergence rate than its ablation counterpart. With the trainable knowledge controller, the prior knowledge is constantly optimized during training, enhancing the adaptation of the provided knowledge to the current task. As a result, agents can make better use of proposed knowledge and learn faster.

4.3.2 Human suboptimal knowledge influence

In this section, we explore the impact of suboptimal human knowledge and how our approach addresses inappropriate knowledge. The ablation results are described in Figure 7(b). To assess the effect of knowledge quality, we consider human knowledge with more fuzzy logic rules to be more comprehensive. For fairness, these rules are inherited among ablation approaches. For example, 'hhkIQL (8 rules)' will include the 5 rules used in 'hhkIQL (5 rules)', and so forth. Since knowledge from humans is highly subjective, it is hard to judge whether the transferred knowledge is inappropriate. To identify how our approach deals with inappropriate knowledge, we substitute the values in human preference vectors Q i F with random value to represent inappropriate knowledge, which is denoted as 'hhkIQL (random)' in Figure 7(b).

As demonstrated in Figure 7(b), more comprehensive human knowledge can help agents achieve better performance. Although 'hhkIQL (1 rules)' can achieve faster learning speed than 'hhkIQL (3 rules)' and 'hhkIQL (5 rules)', it results in lower final performance. We guess that with fewer rules applied, there is a reduced learning burden on knowledge utilization, but this also leads to lower final outcomes. Notably, even though more comprehensive human knowledge is beneficial, the performance of these approaches remains similar. Furthermore, as the learning curve of 'hhkIQL (random)' indicates, the knowledge integration module can efficiently filter out negative knowledge. While it takes agents more time to learn how to utilize the proposed knowledge, 'hhkIQL (random)' can still outperform the baseline algorithm, highlighting the importance of the knowledge integration module. In conclusion, our approach does not rely on high-quality human knowledge, and the knowledge integration module can successfully mitigate the negative knowledge transfer problem, allowing humans to propose any knowledge they consider useful for agents.

5 Conclusion and future work

In this study, we introduce a novel hierarchical learning framework for enhancing coordination in large-scale MAS by leveraging suboptimal human knowledge. This framework consists of the group controller, the knowledge controller, and the knowledge integration, allowing humans to provide knowledge at the top level while agents develop their own policies at the bottom. Evaluated in SMAC with three famous algorithms, our end-to-end methods surpass corresponding baselines in learning speed and final performance, even with low-performance human knowledge integrated. Furthermore, this framework successfully improves the scalability of algorithm, handling scenarios with numerous agents where standard MARL algorithms fail. In the future, we will apply our approach in domains with even more agents involved, and explore its application to heterogeneous agents.

Acknowledgment

This work was supported in part by the Ministry of Education, Culture, Sports, Science and Technology-Japan, Grant-in-Aid for Scientific Research under grant #JP24H00741, and part by the commissioned research by National Institute of Information and Communications Technology (NICT), JAPAN.

References

Yong Liu et al. Multi-agent game abstraction via graph attention neural network. In Proceedings of the 34th AAAI Conference on Artificial Intelligence , pages 7211-7218, 2020. doi: 10.1609/AAAI.V34I05.6211.

Jiang Hao and Pradeep Varakantham. Hierarchical value decomposition for effective on-demand ride-pooling. In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems , pages 580-587, 2022. URL https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p580.pdf.

Dongqin Zhou and Vikash V Gayah. Scalable multi-region perimeter metering control for urban networks: A multi-agent deep reinforcement learning approach. Transportation Research Part C: Emerging Technologies , page 104033, 2023. doi: 10.1016/j.trc.2023.104033.

Qian Long et al. Evolutionary population curriculum for scaling multi-agent reinforcement learning. In Proceedings of the 8th International Conference on Learning Representations , 2020. URL https://openreview.net/forum?id=SJxbHkrKDH.

Kai Cui et al. A survey on large-population systems and scalable multi-agent reinforcement learning. arXiv , 2022. URL https://doi.org/10.48550/arXiv.2209.03859.

Ankur Deka and Katia Sycara. Natural emergence of heterogeneous strategies in artificially intelligent competitive teams. In Advances in Swarm Intelligence: 12th International Conference , pages 13-25, 2021. doi: 10.1007/978-3-030-78743-1_2.

Filippos Christianos et al. Scaling multi-agent reinforcement learning with selective parameter sharing. In Proceedings of the International Conference on Machine Learning , pages 19891998, 2021. URL http://proceedings.mlr.press/v139/christianos21a.html.

Weixun Wang et al. From few to more: Large-scale dynamic multiagent curriculum learning. In Proceedings of the 34th AAAI Conference on Artificial Intelligence , pages 7293-7300, 2020. doi: 10.1609/AAAI.V34I05.6221 .

Xiaoqiang Wang et al. Attention based large scale multi-agent reinforcement learning. In Proceedings of the 5th International Conference on Artificial Intelligence and Big Data , pages 112-117, 2022. doi: 10.1109/ICAIBD55127.2022.9820093.

Sven Gronauer and Klaus Diepold. Multi-agent deep reinforcement learning: a survey. Artificial Intelligence Review , pages 1-49, 2022. doi: 10.1007/s10462-021-09996-w.

Felipe Leno Da Silva and Anna Helena Reali Costa. A survey on transfer learning for multiagent reinforcement learning systems. Journal of Artificial Intelligence Research , pages 645-703, 2019. doi: 10.1613/jair.1.11396.

Andrea Soltoggio et al. A collective AI via lifelong learning and sharing at the edge. Nature Machine Intelligence , pages 251-264, 2024. doi: 10.1038/s42256-024-00800-2.

Haobin Shi et al. Lateral transfer learning for multiagent reinforcement learning. IEEE Transactions on Cybernetics , pages 1699-1711, 2021. doi: 10.1109/TCYB.2021.3108237.

Ruohan Zhang et al. Leveraging human guidance for deep reinforcement learning tasks. In Proceedings of the International Joint Conference on Artificial Intelligence , pages 6339-6346, 2019. doi: 10.24963/IJCAI.2019/884.

Claire Glanois et al. A survey on interpretable reinforcement learning. Machine Learning , pages 1-44, 2024. doi: 10.1007/s10994-024-06543-w.

Jiaming Song et al. Multi-agent generative adversarial imitation learning. Advances in neural information processing systems , pages 7472-7483, 2018. URL https://proceedings.neurips.cc/paper/2018/hash/240c945bb72980130446fc2b40fbb8e0Abstract.html.

Bo Yang, Chaofan Ma, and Xiaofang Xia. Drone formation control via belief-correlated imitation learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems , pages 1407-1415, 2021. doi: 10.5555/3463952.3464114.

Saeid Nahavandi. Trusted autonomy between humans and robots: Toward human-on-the-loop in robotics and autonomous systems. IEEE Systems, Man, and Cybernetics Magazine , pages 10-17, 2017. doi: 10.1109/MSMC.2016.2623867.

Jingliang Duan et al. Hierarchical reinforcement learning for self-driving decision-making without reliance on labelled driving data. IET Intelligent Transport Systems , pages 297-305, 2020. doi: 10.1049/iet-its.2019.0317.

Peng Zhang et al. Kogun: Accelerating deep reinforcement learning via integrating human suboptimal knowledge. In Proceedings of the 29th International Joint Conference on Artificial Intelligence , pages 2291-2297, 2020. doi: 10.24963/IJCAI.2020/317.

Hao Chen et al. Efficient use of heuristics for accelerating xcs-based policy learning in markov games. Swarm and Evolutionary Computation , page 100914, 2021. doi: 10.1016/J.SWEVO.2021.100914.

Milan R Dimitrijevic, Yuri Gerasimenko, and Michaela M Pinter. Evidence for a spinal central pattern generator in humans a. Annals of the New York Academy of Sciences , pages 360-376, 1998. doi: 10.1111/j.1749-6632.1998.tb09062.x.

Chao Yu. Hierarchical mean-field deep reinforcement learning for large-scale multiagent systems. In Proceedings of the 37th AAAI conference on artificial intelligence , pages 1174411752, 2023. doi: 10.1609/AAAI.V37I10.26387.

Mikayel Samvelyan et al. The starcraft multi-agent challenge. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems , pages 2186-2188, 2019. URL http://dl.acm.org/citation.cfm?id=3332052.

Frans A Oliehoek and Christopher Amato. A concise introduction to decentralized POMDPs . Springer International Publishing, 2016. doi: 10.1007/978-3-319-28929-8.

Lotfi A Zadeh. Knowledge representation in fuzzy logic. In An introduction to fuzzy logic applications in intelligent systems , pages 1-25. 1992. doi: 10.1007/978-1-4615-3640-6_1.

Jingchen Li, Haobin Shi, and Kao-Shing Hwang. Using fuzzy logic to learn abstract policies in large-scale multiagent reinforcement learning. IEEE Transactions on Fuzzy Systems , pages 5211-5224, 2022. doi: 10.1109/TFUZZ.2022.3170646.

Sheng Li et al. Deep implicit coordination graphs for multi-agent reinforcement learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems , page 764-772, 2021. doi: 10.5555/3463952.3464044.

Tabish Rashid et al. QMIX: monotonic value function factorisation for deep multi-agent reinforcement learning. In Proceedings of the 35th International Conference on Machine Learning , pages 4292-4301, 2018. URL http://proceedings.mlr.press/v80/rashid18a.html.

Ardi Tampuu et al. Multiagent cooperation and competition with deep reinforcement learning. PloS one , page e0172395, 2017. doi: 10.1371/journal.pone.0172395.

Yaodong Yang et al. Qatten: A general framework for cooperative multiagent reinforcement learning. arXiv , 2020. URL https://arxiv.org/abs/2002.03939.

Lotfi A Zadeh. Fuzzy sets. Information and control , pages 338-353, 1965. doi: 10.1016/S00199958(65)90241-X.

Ahmed Hussein et al. Imitation learning: A survey of learning methods. ACM Computing Surveys (CSUR) , pages 1-35, 2017. doi: 10.1145/3054912.

Hoang M Le et al. Coordinated multi-agent imitation learning. In Proceedings of the International Conference on Machine Learning , pages 1995-2003, 2017. URL http://proceedings.mlr.press/v70/le17a.html.

Shao Zhifei and Er Meng Joo. A survey of inverse reinforcement learning techniques. International Journal of Intelligent Computing and Cybernetics , pages 293-311, 2012. doi: 10.1108/17563781211255862.

Halit Bener Suay et al. Learning from demonstration for shaping through inverse reinforcement learning. In Proceedings of the 15st International Conference on Autonomous Agents and Multiagent Systems , pages 429-437, 2016. URL http://dl.acm.org/citation.cfm?id=2936988.

Travis Mandel et al. Where to add actions in human-in-the-loop reinforcement learning. In Proceedings of the 31st AAAI Conference on Artificial Intelligence , pages 2322-2328, 2017. doi: 10.1609/aaai.v31i1.10945.

Hoang Le et al. Hierarchical imitation and reinforcement learning. In Proceedings of the International conference on machine learning , pages 2917-2926, 2018. URL http://proceedings.mlr.press/v80/le18a.html.

Shubham Pateria et al. Hierarchical reinforcement learning: A comprehensive survey. ACM Computing Surveys , pages 1-35, 2021. doi: 10.1145/3453160.

A Appendix / supplemental material

A.1 Fuzzy logic

Since human knowledge is highly abstract and uncertain, it is inappropriate to use hard rules to represent such prior knowledge [26]. Different from crisp sets, fuzzy logic, based on fuzzy set theory, can apply partial membership functions to represent fuzzy knowledge [32]. For a fuzzy set F , the x in it can be described by a membership function µ F ( x ) with range from 0 to 1, allowing the element partially belong to it:

where X refers to the universal set in a specific problem.

The fuzzy logic rule is usually in the form of 'IF X is A and Y is B THEN Z is C '. Here, ' X is A ' and ' Y is B ' are called preconditions of the fuzzy rule, and ' Z is C ' is the conclusion. The X , Y and Z are variables. And the A , B and C are fuzzy sets, also known as linguistic values. For each fuzzy set, it has a membership function µ F to calculate the truth value T of each precondition:

where x 0 and y 0 are observation values for X and Y , and T A and T B are truth values for preconditions ' X is A ' and ' Y is B '. To get the conclusion of this fuzzy rule, it needs to satisfy both preconditions and the conjunction operator is applied:

Finally, we will get the conclusion's strength ω , sometimes seen as the satisfaction level of the rule:

Summarizing, to abstract human prior knowledge with fuzzy logic rules, we need first to design the rules in the form of 'IF . . . THEN . . . ' sentence. Then membership functions µ F should be built for each preconditions to calculate their truth value T . Finally, the conjunction operator min is applied to satisfy all the preconditions and get conclusion's strength ω . Therefore, a fuzzy rule takes the observation values as input and outputs the value of conclusion to illustrate how likely to operate designed actions under current observation.

A.2 Related work

Due to the expensive exploration, knowledge transfer has become an indispensable approach to enhance the scalability of MARL [11, 12]. On the one hand, the most straightforward implementation is to repurpose solutions from previous tasks obtained by agents [13]. On the other hand, various studies also emphasize the reuse of knowledge from auxiliary sources, such as human expert demonstrations [33].

As the "black box" approach is unsuitable for critical applications, the transfer method should be interpretable, prompting an increasing concern on Human-on-the-Loop [15]. By personally executing tasks, humans provide demonstrations for agents to record in state-action pairs which agents can mimic based on imitation learning [33, 34], inverse reinforcement learning [35, 36], and other humanfocused methods [11, 37]. Unfortunately, these mainstream researches require step-by-step action demonstrations, heavily relying on high-quality and comprehensive expert demonstrations [16, 17].

While some efforts have aimed to mitigate the human burden, these solutions are generally limited to single- or two-player scenarios [20, 21, 38]. Fuzzy logic has been applied in previous work for knowledge representation [20], while their focus is on single-agent scenarios and the agent does not have self-policy development ability. As far as we know, the most successful work is from [27], who handle large-scale MAS with fuzzy agents. However, the use of human knowledge is not within their scope and their approach is more akin to agent knowledge transfer. Compared to previous works, our method, which can easily combine with various MARL algorithms, features a hierarchical learning scheme that human suboptimal knowledge is applied at top-level to enhance learning process of large-scale MAS. Based on the hyper-networks in knowledge integration, we are able to combine human preference with agent preference to empower agents with more knowledge selection freedom.

Our work also shares some similarities with the hierarchical RL methods [19, 23, 38, 39]. However, in contrast to these existing studies that pay more attention to decomposing challenging long-horizon tasks into simpler subtasks, our focus here is to connect humans and agents under a hierarchical structure for leveraging human knowledge and achieving more efficient learning in large-scale MAS.

A.3 Symbol meaning

The meanings of symbols in this work is illustrated in Table 1

A.4 Computational resource

In this work, we run our experiments in a computer with a CPU (13th Gen Intel Core i7-13700F 2.10 GHz), GPU (NVIDIA GeForce RTX 4080), and RAM (128GB). It takes us more than 550 GPU hours to finish all the experiments. It's worth mentioning that the '35m vs 40m' scenario is the most time-consuming experiment where a single run requires beyond 9 hours on average.

A.5 Experiment hyperparameter

The hyperparameters for our experiments are shown in Table 2

A.6 Suboptimal human knowledge applied in experiment

For challenging tasks in SMAC, the following 8 pieces of human knowledge are considered:

Attack the closest enemy.

Attack the enemy with the lowest HP.

Get close to the closest enemy.

Get close to the enemy with the lowest HP.

Disperse when many agents are crowded together.

Gather when there are few agents and they are far away.

Get close to the ally who is attacking.

Attack properly to avoid over-attacking.

The abstract knowledge can be represented with fuzzy logic rules as follows:

IF e _ d is small , THEN action is attackEnemyId .

IF e _ hp is small , THEN action is attackEnemyId .

IF e _ clo _ x is PO , THEN action is east ; IF e _ clo _ x is NE , THEN action is west ; IF e _ clo _ y is PO , THEN action is north ; IF e _ clo _ y is NE , THEN action is south .

IF e _ Lhp _ x is PO , THEN action is east ; IF e _ Lhp _ x is NE , THEN action is west ; IF e _ Lhp _ y is PO , THEN action is north ; IF e _ Lhp _ y is NE , THEN action is south .

IF n _ ally is large AND g _ ally _ d is small AND ally _ x is PO , THEN action is west ; IF . . . AND ally _ x is NE , THEN action is east ; IF . . . AND ally _ y is PO , THEN action is south ; IF . . . AND ally _ y is NE , THEN action is north .

IF n _ ally is small AND g _ ally _ d is large AND ally _ x is PO , THEN action is east ; IF . . . AND ally _ x is NE , THEN action is west ; IF . . . AND ally _ y is PO , THEN action is north ; IF . . . AND ally _ y is NE , THEN action is south .

IF ally _ attacking _ x is PO , THEN action is east ; IF ally _ attacking _ x is NE , THEN action is west ; IF ally _ attacking _ y is PO , THEN action is north ; IF ally _ attacking _ y is NE , THEN action is south .

IF n _ potential is large AND n _ attack is proper , THEN action is attackEnemyId .

The membership functions for the fuzzy sets in each rule are elaborated in Figure 8.

(a) e_d is small

(b) e_hp is small

(c) x or y is PO

(d) x or y is NE

(e) n_ally is large

(f) g_ally_d is small

(g) n_ally is small

(h) g_ally_d is large

(i) n_potential is large

(j) n_attack is proper

Memdbenhip function for eeemi/Distance ibs small

Meribdhip fenction for erpmyfI s small

Mesbembip finetion for x el y is positive

Membenlip functios for x or /y is ncgative

(0.6667,|0.1)

(1.0.1)

Membenhip fueciin for mcan dotueeeiof allies is seul

(1.4.3)

Me

Menbernhip fakctioe Sor men dotupce ef alles is large

(2, 1)

(5,0.1)

(0.5, 0)

Mempenhip functice foxr npmiber of agonis that dis enemy cas be

(0, 0.1)

osl g peg as iaxe eg ppopx ane[ mg sgfe yt,oqune xey ujgy duoiuog

(0,0.6)

(4.0.3)

(5, 0.26)

(F.0.2143)

A.7 Dynamic graph

The full image of the dynamic graph based on group controller is elaborated in Figure 9.

A.8 Limitations and broader impact

In this section, we will discuss the potential limitations of this work, which we aim to address in future research. First, as the proposed modules are shared among agents, we assume that the agents are homogeneous to alleviate the difficulty of knowledge design and computation complexity. However, exploring our approach with heterogeneous agents, which may require different kinds of knowledge, is an interesting direction. Second, even though fuzzy logic is a promising technique for knowledge abstraction, it is relatively primitive, and a better representation method is required to further improve performance, which is a consideration for future work. Third, in this work, we consider integrating suboptimal human knowledge to improve the performance of MARL algorithm and propose a hyper-network to avoid negative knowledge transfer. However, as illustrated in our ablation studies, more comprehensive knowledge should be beneficial. Therefore, discussing what kinds of knowledge are more appropriate and how to design effective knowledge is an interesting topic for future exploration. Finally, due to computational limitations, we only verify our approach in SMAC. Although we have applied ablation studies to enhance convincingness, it would be helpful to conduct experiments in other domains with more agents involved, which we plan for future work.

This work aims to contribute to the development of MARL algorithms. As with any field in machine learning, it is possible that improving the capabilities of these algorithms could lead to unethical uses. However, there are also many potential benefits to better cooperative AI, such as applications in disaster rescue robots among others. We believe that the potential benefits of developing more capable and cooperative AI outweigh the potential risks.

10

= 17

27

t = 28

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: Yes, the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: Yes, the limitations of this work are discussed. More details can be found in the appendix (Section A.8).

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory Assumptions and Proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

Justification: From our perspective, this paper does not include theoretical results. The primary contribution of this work is not theoretical study. Instead, we base our method on well-verified theories from other works.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental Result Reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: Yes, the information and experiment details have been fully disclosed. More details can be found in the method part (Section 3), experiment setting part (Section 4.1), and appendix (Section A.3, A.5, and A.6).

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [No]

Justification: As our other work is related to this one, we prefer not to disclose the code currently before publishing further related research. However, for the reviewers, we provide access to the code in the supplemental material (instructions can be found in the 'README.txt' file).

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental Setting/Details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: Yes, all experiment details have been specified. More details can be found in experiment setting part (Section 4.1), and appendix (Section A.5 and A.6).

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment Statistical Significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: Yes, error bars and other appropriate information have been reported suitably and correctly. More details can be found in the experiment part (Section 4) and corresponding figures (Figure 4, 5, and 7).

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments Compute Resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: Yes, the sufficient information on the computer resources has been provided. More details can be found in appendix (Section A.4).

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code Of Ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: Yes, this work follows the NeurIPS Code of Ethics.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader Impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: Yes, we have attempted to discuss both potential positive societal impacts and negative societal impacts. More details can be found in appendix (Section A.8).

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: From our perspective, this paper poses no such risks.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: Yes, all existing assets are open access and the original papers have been cited.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New Assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [NA]

Justification: For our best knowledge, this paper does not release new assets.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and Research with Human Subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: As far as we know, this paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: As far as we know, this paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors proposed a new framework (hhk-MARL) that integrates human abstract knowledge with hierarchical reinforcement learning to address the learning challenges in large-scale multi-agent systems. The framework employs fuzzy logic to represent human knowledge and a graph-based group controller to enhance agent coordination. Experimental results in the StarCraft Multi-agent Challenge demonstrate that the proposed approach significantly accelerates the training process and improves the final performance of the agents, even with imperfect human prior knowledge.\n\nSTRENGTHS:\n* Overall, the proposed hhk-MARL framework effectively integrates human knowledge with hierarchical reinforcement learning, providing a flexible and adaptive approach for multi-agent systems.\n\n* Methodologically, the authors use fuzzy logic and hypernetworks to dynamically generate weights based on agent observations allowing for the seamless combination of human and agent preferences.\n\n* In the experimental results, the comprehensive experiments demonstrate the framework's efficacy and robustness, showing significant improvements over baseline methods in various scenarios.\n\nWEAKNESSES:\nWhile the paper is well-written overall, it lacks detailed comparisons with prior research and thorough explanations of the human knowledge used. Specifically, section 3.1 does not clearly justify the choice of fuzzy logic over other approaches, which could also handle uncertainty and abstract knowledge effectively. Additionally, the paper can explain how its approach differs from previous works such as KoGuN and Shi 2021, to clarify its unique contributions. In section 3.2, the new aspects of using hypernetworks for knowledge integration should be more distinctly highlighted in comparison to similar methodologies. For the details, see the following Questions.\n\nQUESTIONS:\n1. The paper employs fuzzy logic in the hhk-MARL framework for integrating human knowledge, but it does not provide a clear explanation in section 3.1 for choosing fuzzy logic over other methods such as Bayesian networks, a combination of reinforcement learning and heuristics, or neural network-based knowledge representation. These alternative methods could also handle uncertainty and abstract knowledge effectively. A more detailed justification for selecting fuzzy logic would enhance the understanding of its advantages in this context.\n\n2. The idea of incorporating fuzzy logic into hierarchical RL has been introduced in prior works such as KoGuN [20] and in Shi 2021 [a]. However, it is essential to explain the differences between these approaches and the proposed method in section 3.1. Highlighting these distinctions will clarify the unique contributions of this work and demonstrate how it advances beyond existing methodologies.\n\n3. In section 3.2 on Knowledge Integration, it is important to highlight the novel aspects of the proposed method. The integration of human knowledge using hypernetworks to dynamically generate weights based on agent observations presents a unique approach. However, the methodology bears similarities to existing works such as HAMXCS [21] which also incorporates heuristic knowledge into reinforcement learning, Hierarchical RL for Self-Driving [19] which uses hierarchical reinforcement learning for decision-making, and KoGuN, which refines fuzzy logic rules with hypernetworks. A clearer distinction of the innovative elements in comparison to these related works would strengthen the contribution of this paper.\n\n4. Appendix A.6 presents the suboptimal human knowledge rules, but it is unclear who determined these rules and to what extent they are incomplete or reasonable. Given that they are labeled as suboptimal, a clearer explanation of their limitations and adequacy is necessary, especially for readers unfamiliar with Starcraft. This information is crucial for understanding the potential applicability and adaptation of these rules to other tasks.\n\nLIMITATIONS:\nThe authors have adequately addressed the limitations and potential negative societal impact of their work.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper integrates an human in the loop to provide knowledge that improves learning in marl. This is done through a hierarchical structure, but ultimately it is up to the agents the final decision of accepting the human suggestions (hierarchy comes from human knowledge to agents). Overall, there is an integration of human knowledge with what the agents learn.\n\nSTRENGTHS:\nThe paper is well organized and it is interesting. The idea of integrating human knowledge is this kind of tasks is interesting and it makes sense. Mostly, the paper is easy to follow and understand, with some exception that I outline below.\n\nWEAKNESSES:\nWhile I find this paper interesting, I have some remarks, as noted below:\n* in line 131 it is stated that $M_L^I$ corresponds to a fuzzy set; however, it is not clear what $M$ is exactly, and how it relates to fuzzy logic in this context; it is not easy to understand how the relation between $O$ and $M$ is calculated and how is the membership function $\\mu$ implemented\n* from my understanding, when integrating the proposed approach with IQL, there is not a mixer as in the other value function factorization methods like QMIX; this leads me to think that the lack of a mixer could be the reason behind the huge improvements seen in Figure 4 for IQL when combined with this method; can it mean that the mixer can be slightly detrimental in the overall framework?\n\nQUESTIONS:\nIn addition to the points above, I have some specific questions that I would like the authors to comment on:\n* what are the limitations of considering only a small set of human opinions, like the 8 ones defined in appendix 6?\n* if a certain human knowledge concerns only one specific agent, is it still given to the others as well? if yes, how does it affect learning? do the others get confused by that knowledge?\n* how do the agents decide if they accept a certain human suggestion? is it a joint decision? if yes, can they reason accurately if the decision concerns a specific agent, but has nothing to do with the others?\n* is the knowledge controller deterministic? or was it trained a priori with human knowledge?\n* it is stated in lines 179-180 that the group controller is used only during training to not violate CTDE; what about the knowledge integration, is it also used only during training? or also during execution? since it receives human knowledge from all agents too\n* the knowledge integration module seems to follow an interesting approach, since the weights of the second network are generated by an initial network based on the observations of the agents given as inputs; could the authors elaborate on the motivations for this approach? why are the weights generated from a first network and not only using one network?\n* i can see the authors focused in environments involving marines only; is there a specific reason for that? is it because of the availability of prior human knowledge? or how hard it would be to create a knowledge controller network for other cases?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors propose a novel method to tackle the multi-agent reinforcement learning problem. They do so by combining human abstract knowledge with hierarchical reinforcement learning. Specifically, human knowledge in the form of fuzzy logic rules is combined, at the top level, with each individual agent’s decisions, learned at the bottom level. Then, a graph-based group controller performs agent coordination to decide what the action at each step should be. The authors evaluate the proposed method on the StarCraft multi-agent Challenge, combined with three algorithms (IQL, QMIX, and Qatten). The results indicate that the proposed approach is capable of improving the overall performance.\n\nSTRENGTHS:\nThe authors tackle the multi-agent reinforcement learning problem in a creative and novel way, combining human feedback with hierarchical techniques. Moreover, it is a very interesting idea to provide a general algorithm that can be coupled with any existing MARL technique. In this way, it is possible to take advantage of the benefits of previously proposed algorithms while also incorporating new ideas to improve overall performance.\n\nThe paper is well-written, and all the high-level ideas behind the different components are well-explained.\n\nWEAKNESSES:\nThe empirical evaluation is thorough, and it indicates that the claims are correct. However, only three samples are rarely enough to draw strong conclusions.\n\nI believe some crucial parts of the algorithm were not very detailed. In particular, what are the learning rules/loss functions for each of the components? That is, how is $\\beta$ trained? How is the knowledge integration component trained? With the current description, I believe it would be very hard for someone to replicate the method.\n\nQUESTIONS:\nFor the ablation studies of the impact of each component on the final performance, it would also be interesting to see how much of the agent’s Q is used vs how much of the human knowledge Q is used for the final Q prediction. That is, what is the magnitude of the impact that the human knowledge component has over the Q predictions?\n\nLIMITATIONS:\nThe authors discuss the limitations of the work. No major concerns.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 2, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-19"}