Actor-Attention-Critic for Multi-Agent Reinforcement Learning (1810.02912v2)

Abstract: Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep. This attention mechanism enables more effective and scalable learning in complex multi-agent environments, when compared to recent approaches. Our approach is applicable not only to cooperative settings with shared rewards, but also individualized reward settings, including adversarial settings, as well as settings that do not provide global states, and it makes no assumptions about the action spaces of the agents. As such, it is flexible enough to be applied to most multi-agent learning problems.

• The paper presents the Actor-Attention-Critic framework that integrates decentralized policies with a centralized, attention-based critic to enhance coordinated learning.
• It employs a multi-headed attention mechanism to dynamically select relevant agent interactions, outperforming methods like MADDPG and COMA.
• Empirical evaluations demonstrate that MAAC scales effectively across varying agent counts, showing robust performance in both cooperative and competitive environments.

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

The paper, authored by Shariq Iqbal and Fei Sha, presents a novel approach to Multi-Agent Reinforcement Learning (MARL) called Actor-Attention-Critic (MAAC). This approach seeks to address the inherent complexities and non-stationarity associated with multi-agent environments by incorporating an attention mechanism within a centralized critic framework. The following essay provides a detailed analysis, evaluation, and exploration of the paper's contributions to the field.

Overview of Approach

Multi-agent environments present distinctive challenges such as non-stationarity and dynamically shifting agent interactions. Traditional paradigms like independent learning and fully centralized control are either inadequate due to their violation of Markovian assumptions or unscalable due to combinatorial action spaces. The MAAC method offers a middle ground by coupling decentralized policy training with centralized Q-value critic updates. These critics utilize an attention mechanism that dynamically selects relevant agent information, enhancing scalability and adaptability across cooperative and competitive tasks.

The authors employ a multi-headed attention mechanism to facilitate the critic's focus on relevant agents, with shared parameters across critics promoting multi-task learning. The centralized critics aggregate information from embedded agent observations and actions, enabling agents to prioritize their interactions based on context rather than processing uniform or complete information.

Numerical Results

Empirical evaluations within simulated environments demonstrate the practical advantages of MAAC over competing methods such as MADDPG and COMA. Notably, in the Cooperative Treasure Collection task, MAAC achieved a significant improvement in performance, with scalability evidenced across varying agent counts. Performance metrics across different agent counts affirm the model's capability to maintain efficiency and adaptability as complexity increases.

The attention mechanism's efficacy is further illustrated in a Rover-Tower task, where the model demonstrated superior agent pairing and coordination without explicit communication. MAAC's ability to handle dynamic attention needs, by implicitly learning agent relevance, underscores its robustness in complex problem spaces.

Implications and Future Directions

The MAAC framework presents promising implications for multi-agent scenarios where adaptive interaction models are crucial, such as autonomous vehicles, collaborative robotics, and distributed sensor networks. Its ability to scale with agent numbers and dynamically adjust attention allocation opens avenues for developing more robust, flexible multi-agent systems.

Future research could explore the integration of MAAC with hybrid systems combining rule-based decision frameworks. Additionally, expansions into environments with larger agent clusters or sub-societal structures might benefit from hierarchical attention schemes, leveraging the strengths of mixed decentralization and centralized policy optimizations.

Conclusion

The Actor-Attention-Critic paradigm introduces a technically sound and empirically validated approach to multi-agent reinforcement learning, catering to the dynamic and interactive dimensions of real-world environments. By bridging the gap between decentralized decision-making and centralized learning processes, it formulates a comprehensive toolkit for advancing MARL methodologies. The paper's contributions mark a substantial progression in simplifying and optimizing multi-agent interactions, with potential applications spanning diverse domains requiring collaborative intelligence.