Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration (2505.05262v2)

Abstract: Learning to cooperate in distributed partially observable environments with no communication abilities poses significant challenges for multi-agent deep reinforcement learning (MARL). This paper addresses key concerns in this domain, focusing on inferring state representations from individual agent observations and leveraging these representations to enhance agents' exploration and collaborative task execution policies. To this end, we propose a novel state modelling framework for cooperative MARL, where agents infer meaningful belief representations of the non-observable state, with respect to optimizing their own policies, while filtering redundant and less informative joint state information. Building upon this framework, we propose the MARL SMPE algorithm. In SMPE, agents enhance their own policy's discriminative abilities under partial observability, explicitly by incorporating their beliefs into the policy network, and implicitly by adopting an adversarial type of exploration policies which encourages agents to discover novel, high-value states while improving the discriminative abilities of others. Experimentally, we show that SMPE outperforms state-of-the-art MARL algorithms in complex fully cooperative tasks from the MPE, LBF, and RWARE benchmarks.

Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration

In the domain of multi-agent deep reinforcement learning (MARL), cooperation in environments characterized by partial observability and absence of communication channels presents formidable challenges. This paper proposes a novel approach to address these difficulties by focusing on state modelling and adversarial exploration, which collectively aim to enhance the exploration capabilities and policy effectiveness of agents within MARL settings.

Technical Contributions

The authors introduce a new framework for cooperative MARL that enables agents to infer latent belief representations of the non-observable state by optimizing their own policies. This state modelling framework is designed to filter out redundant joint state information that could potentially hinder performance. Following this conceptual framework, the paper presents the MARL SMPE$^2$ algorithm, which enhances agents' abilities to discriminate between states under partial observability. It achieves this explicitly by incorporating inferred state beliefs into the policy network and implicitly by adopting an adversarial exploration strategy. This twin approach encourages agents to seek novel, high-value states while concurrently refining the ability of other agents to discriminate between these states.

Experimental Results and Analysis

Empirical evaluations demonstrate that the SMPE$^2$ algorithm achieves superior results compared to existing state-of-the-art MARL algorithms across complex fully cooperative tasks in the MPE, LBF, and RWARE benchmarks. These results underscore the efficacy of the algorithm's dual approach to maximizing collaborative task execution policies and exploration capabilities, thus highlighting potential avenues for improving MARL performance in similar settings.

Considerations and Implications

The strong numerical results presented in the paper suggest that incorporating state belief representations and adversarial exploration strategies significantly bolsters MARL effectiveness. By encouraging agents to discover novel states that contribute positively to the joint task, SMPE$^2$ aligns intrinsic rewards with cooperative objectives—a strategy that mitigates issues stemming from partial observability and enhances adaptability of MARL models to complex tasks.

Future Directions

For future work, the paper opens up several areas for further research, notably:

1. Architectural Integrations: Exploring the integration of transformers within the proposed state modelling framework could advance the scalability and accuracy of learned representations.
2. Scalability and Robustness: Investigating the framework's adaptability to larger agent populations and more complex environments could yield insights about the robustness and scalability of the algorithm.
3. Application in Stochastic Domains: Application of the framework in settings with stochastic dynamics and noisy observations remains unexplored, presenting another area of interest.

Conclusion

In summary, this paper offers a sophisticated approach to addressing fundamental challenges in cooperative MARL. Through its innovative state modelling and adversarial exploration techniques, it substantially contributes to enhancing the discriminative power and collaborative efficiency of agents operating in decentralized, partially observable environments. As the field of MARL continues to evolve, the methodologies proposed herein will likely inspire further research into state representation learning as a means to solve the persistent challenges associated with multi-agent reinforcement learning.