Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning (1906.04737v1)

Abstract: Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which has received increasing attention is multi-agent reinforcement learning, in which multiple agents learn concurrently to coordinate their actions. In such multi-agent environments, additional learning problems arise due to the continually changing decision-making policies of agents. This paper surveys recent works that address the non-stationarity problem in multi-agent deep reinforcement learning. The surveyed methods range from modifications in the training procedure, such as centralized training, to learning representations of the opponent's policy, meta-learning, communication, and decentralized learning. The survey concludes with a list of open problems and possible lines of future research.

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

The paper "Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning" authored by Georgios Papoudakis et al., centered on the complexities introduced by non-stationarity in multi-agent deep reinforcement learning (MADRL). This is a critical problem as agents adapt their policies during training, thereby affecting the perceived dynamics of the environment and challenging existing reinforcement learning (RL) frameworks that are predominantly designed under the assumption of stationary environments.

Overview

Multi-agent systems are prevalent in domains ranging from autonomous driving and resource allocation to robotics, and require efficient collaboration and adaptation strategies. In these systems, the continual evolution of agent policies can disrupt the foundational Markov assumption underpinning traditional RL, which ensures that future states depend solely on the current state and action, rendering conventional approaches ineffective.

The paper surveys a breadth of strategies cited for their ability to surmount non-stationarity. These include centralized training methodologies, decentralized learning approaches, opponent modeling, and innovations in communication strategies. Each of these strategies is dissected to illustrate how they target specific non-stationary challenges.

Key Methodologies

The paper delineates several categories of approaches:

• Centralized Critic Techniques: Centralized critics, such as MADDPG, leverage joint modeling during the training phase to stabilize learning by providing agents access to observations and actions of all agents in an environment. This framework supports decentralized policy execution, which is crucial for scalability.
• Decentralized Learning Techniques: Decentralized approaches like self-play and stabilized experience replay operate under the principle that agents can autonomously adapt without relying on the centralized observation, sidestepping scalability issues intrinsic to centralized techniques.
• Opponent Modeling: In enhancing performance in multi-agent setups, such models predict the actions and strategies of other agents. Techniques like LOLA take this further by incorporating learning dynamics of opponents into the agent's decision-making process.
• Meta-Learning: Methods such as MAML offer adaptability by optimizing agents' initial learning states to facilitate rapid policy adaptation across changing dynamics.
• Communication: By enabling agents to share information, these strategies address non-stationarity through coordination without strict centralized control, promoting robust agent interactions.

Implications and Future Directions

This paper critically sets the stage for advancing non-stationarity solutions in MADRL. Each strategy has its implications and potential applications within both theoretical and practical contexts involving agent-rich environments. Future research is encouraged to delve into several open questions:

• Open Multi-Agent Systems: As real-world systems often involve dynamically changing agent numbers, approaches addressing these dynamics are necessary to prevent non-stationarity issues arising from agent fluctuations.
• Convergence Guarantees: Theoretical exploration into convergence properties in multi-agent settings is crucial, especially in defining and achieving equilibrium states.
• Limited Opponent Information: Solutions in non-ideal scenarios where agents have constrained access to opponents' observations can significantly broaden MADRL's applicability.
• Credit Assignment: Investigating models capable of decomposing rewards amongst agents collaboratively is essential for optimizing decentralized policy learning.

Conclusion

The paper by Papoudakis et al. serves as a comprehensive survey of strategies to combat non-stationarity in MADRL, offering keen insights into existing methodologies and charting potential research territories. This serves as a promising framework for seasoned researchers aiming to advance multi-agent systems and enhance collaborative agent-learning strategies across varied environmental contexts.