A Survey on Self-play Methods in Reinforcement Learning (2408.01072v1)

Abstract: Self-play, characterized by agents' interactions with copies or past versions of itself, has recently gained prominence in reinforcement learning. This paper first clarifies the preliminaries of self-play, including the multi-agent reinforcement learning framework and basic game theory concepts. Then it provides a unified framework and classifies existing self-play algorithms within this framework. Moreover, the paper bridges the gap between the algorithms and their practical implications by illustrating the role of self-play in different scenarios. Finally, the survey highlights open challenges and future research directions in self-play. This paper is an essential guide map for understanding the multifaceted landscape of self-play in RL.

Overview of Self-play Methods in Reinforcement Learning

The paper provides a comprehensive survey on self-play methods in reinforcement learning (RL), which are techniques that involve agents interacting with copies or past versions of themselves for optimizing decision-making processes. It establishes a foundational understanding of self-play by elucidating the RL framework and basic game theory concepts. Furthermore, the paper delineates a unified framework that classifies existing self-play algorithms, bridging the gap between the theoretical underpinnings and practical implications, while identifying open challenges and future research directions.

Self-play algorithms have become an integral part of modern RL, particularly in transitioning from single-agent to multi-agent reinforcement learning (MARL). The paper highlights that the primary issues in MARL are coordination, communication, and equilibrium selection in competitive scenarios. Addressing these issues through self-play approaches offers the potential for a more stable and manageable learning process.

Key Concepts and Framework

The survey begins by exploring the RL framework's use of Markov Decision Processes (MDPs) and Markov games as mathematical models describing the environment in terms of states, actions, transitions, and rewards. It emphasizes how self-play can be applied within these frameworks by allowing agents to optimize policies through iterative interactions with themselves.

Within this context, the paper discusses the significance of game theory in understanding agent interactions and decision-making in environments characterized by multiple decision-makers. This theoretical backdrop supports the classification and evaluation of self-play approaches.

The unified framework proposed in this paper is crucial for categorizing self-play algorithms. It provides a structured way to comprehend how these algorithms extend beyond traditional methods to incorporate evolved strategies like the PSRO series, ongoing-training-based methods, and regret-minimization-based approaches. Interestingly, the paper places strong emphasis on evolving self-play strategies seeking to optimize computational efficiency and achieve convergence in complex multi-agent environments.

Classification and Analysis of Self-play Algorithms

1. Traditional Self-play Algorithms: These involve agents improving their strategies through iterative interactions with their historical versions. They serve as a foundation for more advanced approaches, demonstrating effectiveness in transitive scenarios but sometimes struggling with non-transitive dynamics.
2. PSRO Series: An extension of the double oracle framework, PSRO emphasizes strategic diversity and equilibrium refinement through player adaptations over iterative cycles of play. This series allows more sophisticated interaction methods, accommodating broader game-theoretic concepts like correlated equilibria.
3. Ongoing-training-based Series: This approach diverges from traditional and PSRO methods by focusing on continuous training processes rather than iterative expansion of policy sets. This series enhances adaptability and robustness, improving training efficiency across varying scales.
4. Regret-minimization-based Series: These algorithms focus on long-term payoff optimization rather than emphasizing single episodes. They employ strategies such as CFR (Counterfactual Regret Minimization) for extensive games to reduce cumulative regrets over time, enabling more aggressive play needed in complex games with imperfect information.

Empirical Applications and Implications

The application of self-play transcends theoretical constructs when applied to board games, card games, and video games, demonstrating substantial real-world performance gains. Classic examples include remarkable triumphs in Go by AlphaGo and AlphaZero, which leverage self-play for mastering strategic depth in board games. In the domain of card games, methodologies such as those showcased by Libratus and Deepstack in poker exemplify the prowess of self-play in achieving superhuman abilities through iterative self-optimization. Moreover, video games like StarCraft II and Dota 2 illustrate the potential of self-play to master complex, dynamic environments characterized by multifaceted interactions.

Conclusion and Future Directions

The survey underscores the importance of self-play as a pivotal mechanism in advancing RL, noting its ability to navigate complex environment dynamics that arise with scaling MARL systems. The authors encourage further exploration into more computationally efficient methods for equilibrium computation and real-time adaptability, especially in non-transitive and multi-agent settings. Future research might also focus on integrating self-play techniques with LLMs to harness their potential for developing AI systems with both enhanced strategic reasoning capabilities and practical applicability.

In sum, the survey presents a detailed, methodical analysis of self-play methods, suggesting it remains an essential approach in evolving the landscape of artificial intelligence towards more autonomous, self-sufficient learning systems.