Provable Self-Play Algorithms for Competitive Reinforcement Learning

12. Červenec 2020

Řečníci

O prezentaci

Self-play, where the algorithm learns by playing against itself without requiring any direct supervision, has become the new weapon in modern Reinforcement Learning (RL) for achieving superhuman performance in practice. However, the majority of exisiting theory in reinforcement learning only applies to the setting where the agent plays against a fixed environment. It remains largely open whether self-play algorithms can be provably effective, especially when it is necessary to manage the exploration/exploitation tradeoff. We study self-play in competitive reinforcement learning under the setting of Markov games, a generalization of Markov decision processes to the two-player case. We introduce a self-play algorithm—Value Iteration with Upper/Lower Confidence Bound (VI-ULCB), and show that it achieves regret Õ(√(T)) after playing T steps of the game. The regret is measured by the agent's performance against a fully adversarial opponent who can exploit the agent's strategy at any step. We also introduce an explore-then-exploit style algorithm, which achieves a slightly worse regret of Õ(T^2/3), but is guaranteed to run in polynomial time even in the worst case. To the best of our knowledge, our work presents the first line of provably sample-efficient self-play algorithms for competitive reinforcement learning.

Organizátor

Kategorie

O organizátorovi (ICML 2020)

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

Uložení prezentace

Měla by být tato prezentace uložena po dobu 1000 let?

Jak ukládáme prezentace

Pro uložení prezentace do věčného trezoru hlasovalo 0 diváků, což je 0.0 %

Sdílení

Doporučená videa

Prezentace na podobné téma, kategorii nebo přednášejícího

Zajímají Vás podobná videa? Sledujte ICML 2020