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 …
Recent advances in reinforcement learning in finance
The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …
revolutionized the techniques on data processing and data analysis and brought new …
On the theory of policy gradient methods: Optimality, approximation, and distribution shift
Policy gradient methods are among the most effective methods in challenging reinforcement
learning problems with large state and/or action spaces. However, little is known about even …
learning problems with large state and/or action spaces. However, little is known about even …
Optimality and approximation with policy gradient methods in markov decision processes
Policy gradient (PG) methods are among the most effective methods in challenging
reinforcement learning problems with large state and/or action spaces. However, little is …
reinforcement learning problems with large state and/or action spaces. However, little is …
Provably efficient exploration in policy optimization
While policy-based reinforcement learning (RL) achieves tremendous successes in practice,
it is significantly less understood in theory, especially compared with value-based RL. In …
it is significantly less understood in theory, especially compared with value-based RL. In …
Independent policy gradient for large-scale markov potential games: Sharper rates, function approximation, and game-agnostic convergence
We examine global non-asymptotic convergence properties of policy gradient methods for
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …
Hybrid rl: Using both offline and online data can make rl efficient
We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has
access to an offline dataset and the ability to collect experience via real-world online …
access to an offline dataset and the ability to collect experience via real-world online …
Pc-pg: Policy cover directed exploration for provable policy gradient learning
Direct policy gradient methods for reinforcement learning are a successful approach for a
variety of reasons: they are model free, they directly optimize the performance metric of …
variety of reasons: they are model free, they directly optimize the performance metric of …
Last-iterate convergence of decentralized optimistic gradient descent/ascent in infinite-horizon competitive markov games
We study infinite-horizon discounted two-player zero-sum Markov games, and develop a
decentralized algorithm that provably converges to the set of Nash equilibria under self-play …
decentralized algorithm that provably converges to the set of Nash equilibria under self-play …
Learning zero-sum simultaneous-move markov games using function approximation and correlated equilibrium
In this work, we develop provably efficient reinforcement learning algorithms for two-player
zero-sum Markov games with simultaneous moves. We consider a family of Markov games …
zero-sum Markov games with simultaneous moves. We consider a family of Markov games …