2 research outputs found
Multi-Agent Actor-Critic with Generative Cooperative Policy Network
We propose an efficient multi-agent reinforcement learning approach to derive
equilibrium strategies for multi-agents who are participating in a Markov game.
Mainly, we are focused on obtaining decentralized policies for agents to
maximize the performance of a collaborative task by all the agents, which is
similar to solving a decentralized Markov decision process. We propose to use
two different policy networks: (1) decentralized greedy policy network used to
generate greedy action during training and execution period and (2) generative
cooperative policy network (GCPN) used to generate action samples to make other
agents improve their objectives during training period. We show that the
samples generated by GCPN enable other agents to explore the policy space more
effectively and favorably to reach a better policy in terms of achieving the
collaborative tasks.Comment: 10 pages, total 9 figures including all sub-figure
A Review of Cooperative Multi-Agent Deep Reinforcement Learning
Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. In this review article, we have mostly focused on
recent papers on Multi-Agent Reinforcement Learning (MARL) than the older
papers, unless it was necessary. Several ideas and papers are proposed with
different notations, and we tried our best to unify them with a single notation
and categorize them by their relevance. In particular, we have focused on five
common approaches on modeling and solving multi-agent reinforcement learning
problems: (I) independent-learners, (II) fully observable critic, (III) value
function decomposition, (IV) consensus, (IV) learn to communicate. Moreover, we
discuss some new emerging research areas in MARL along with the relevant recent
papers. In addition, some of the recent applications of MARL in real world are
discussed. Finally, a list of available environments for MARL research are
provided and the paper is concluded with proposals on the possible research
directions