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 …
Is pessimism provably efficient for offline rl?
We study offline reinforcement learning (RL), which aims to learn an optimal policy based on
a dataset collected a priori. Due to the lack of further interactions with the environment …
a dataset collected a priori. Due to the lack of further interactions with the environment …
A theoretical analysis of deep Q-learning
Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …
foundation is less well understood. In this work, we make the first attempt to theoretically …
Bridging offline reinforcement learning and imitation learning: A tale of pessimism
Offline (or batch) reinforcement learning (RL) algorithms seek to learn an optimal policy from
a fixed dataset without active data collection. Based on the composition of the offline dataset …
a fixed dataset without active data collection. Based on the composition of the offline dataset …
Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …
led to multiple successes in solving sequential decision-making problems in various …
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 …
A review of cooperative multi-agent deep reinforcement learning
Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …
systems in recent years. The aim of this review article is to provide an overview of recent …
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 …
Natural policy gradient primal-dual method for constrained markov decision processes
We study sequential decision-making problems in which each agent aims to maximize the
expected total reward while satisfying a constraint on the expected total utility. We employ …
expected total reward while satisfying a constraint on the expected total utility. We employ …