Self-Play Reinforcement Learning Algorithm

A Self-Play Reinforcement Learning Algorithm is a reinforcement learning algorithm for game play that uses self-play methods.

• Context:
  • It can typically train agents without requiring external opponents or human demonstrations.
  • It can typically generate training experiences by having agents compete against past versions or current versions of themselves.
  • It can typically improve policys through iterative self-improvement where agent performance increases across successive generations.
  • It can typically evaluate new strategys against a distribution of opponents derived from the algorithm's own learning history.
  • It can often implement fictitious self-play where agents learn against a mixture of past policys.
  • It can often incorporate neural networks to approximate value functions and policy functions from self-play experiences.
  • It can often balance exploration and exploitation through search algorithms like Monte Carlo Tree Search combined with learned policys.
  • It can range from being a Simple Self-Play Reinforcement Learning Algorithm to being a Complex Self-Play Reinforcement Learning Algorithm, depending on its self-play experience generation method.
  • It can range from being a Symmetric Self-Play Reinforcement Learning Algorithm to being an Asymmetric Self-Play Reinforcement Learning Algorithm, depending on its self-play role assignment approach.
  • ...
• Examples:
  • Self-Play Reinforcement Learning Algorithm Implementations, such as:
    • AlphaGo Zero (2017), demonstrating neural network guided self-play without human expert data.
    • AlphaZero (2018), extending self-play reinforcement learning to chess, shogi, and Go with a single algorithm.
    • OpenAI Five (2019), applying self-play reinforcement learning to team-based competitive games.
    • Pluribus (2019), implementing self-play reinforcement learning for multi-player poker.
  • Self-Play Reinforcement Learning Algorithm Variants, such as:
    • Population-Based Self-Play Algorithms that maintain a diverse population of agents to avoid strategy cycling.
    • Prioritized Fictitious Self-Play Algorithms that weight opponent selection based on learning potential.
    • Asymmetric Self-Play Algorithms where agents take on different roles to learn cooperative behaviors.
  • ...
• Counter-Examples:
  • Supervised Learning from Expert Demonstrations, which requires human expert data rather than self-generated experiences.
  • Single-Agent Reinforcement Learning Algorithms, which learn from environment interactions without self-competition.
  • Evolutionary Algorithms for game play, which typically use population-based selection rather than policy gradient or value-based learning.
  • Multi-Agent Reinforcement Learning Algorithms with fixed opponents, which don't implement the self-improvement loop characteristic of self-play.
• See: Expert-Play Reinforcement Learning, Reinforcement Learning from Human Feedback, Multi-Agent Reinforcement Learning, Game-Theoretic Learning, Absolute Zero Reasoner.

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References

2017

• (Silver et al., 2017) ⇒ David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis. (2017). “Mastering the Game of Go Without Human Knowledge.” In: Nature, 550(7676).
  • QUOTE: ... These neural networks were trained by supervised learning from human expert movses, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. ...

2016

• (Heinrich & Silver, 2016) ⇒ Johannes Heinrich, and David Silver. (2016). “Deep Reinforcement Learning from Self-play in Imperfect-information Games.” In: Proceedings of NIPS Deep Reinforcement Learning Workshop.
  • QUOTE: Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior work has focused on computing Nash equilibria in a handcrafted abstraction of the domain. In this paper we introduce the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge. Our method combines fictitious self-play with deep reinforcement learning. ...

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