Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - arXiv preprint arXiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
A modern introduction to online learning
F Orabona - arXiv preprint arXiv:1912.13213, 2019 - arxiv.org
In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …
of Online Convex Optimization. Here, online learning refers to the framework of regret …
OpenSpiel: A framework for reinforcement learning in games
OpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player (single …
reinforcement learning and search/planning in games. OpenSpiel supports n-player (single …
Deep counterfactual regret minimization
Abstract Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large
imperfect-information games. It converges to an equilibrium by iteratively traversing the …
imperfect-information games. It converges to an equilibrium by iteratively traversing the …
Heads-up limit hold'em poker is solved
Poker is a family of games that exhibit imperfect information, where players do not have full
knowledge of past events. Whereas many perfect-information games have been solved (eg …
knowledge of past events. Whereas many perfect-information games have been solved (eg …
Student of Games: A unified learning algorithm for both perfect and imperfect information games
Games have a long history as benchmarks for progress in artificial intelligence. Approaches
using search and learning produced strong performance across many perfect information …
using search and learning produced strong performance across many perfect information …
Solving imperfect-information games via discounted regret minimization
N Brown, T Sandholm - Proceedings of the AAAI Conference on Artificial …, 2019 - aaai.org
Counterfactual regret minimization (CFR) is a family of iterative algorithms that are the most
popular and, in practice, fastest approach to approximately solving large …
popular and, in practice, fastest approach to approximately solving large …
Actor-critic policy optimization in partially observable multiagent environments
Optimization of parameterized policies for reinforcement learning (RL) is an important and
challenging problem in artificial intelligence. Among the most common approaches are …
challenging problem in artificial intelligence. Among the most common approaches are …
Safe and nested subgame solving for imperfect-information games
N Brown, T Sandholm - Advances in neural information …, 2017 - proceedings.neurips.cc
In imperfect-information games, the optimal strategy in a subgame may depend on the
strategy in other, unreached subgames. Thus a subgame cannot be solved in isolation and …
strategy in other, unreached subgames. Thus a subgame cannot be solved in isolation and …