Multi-Agent Actor-Critic with Generative Cooperative Policy Network

10/22/2018
by   Heechang Ryu, et al.
0

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.

READ FULL TEXT
research
09/26/2022

More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization

In cooperative multi-agent reinforcement learning (MARL), combining valu...
research
10/31/2021

Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method

We discuss the problem of decentralized multi-agent reinforcement learni...
research
11/06/2022

Decentralized Policy Optimization

The study of decentralized learning or independent learning in cooperati...
research
06/02/2023

Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning

Executing actions in a correlated manner is a common strategy for human ...
research
08/05/2021

Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach

One of the challenges for multi-agent reinforcement learning (MARL) is d...
research
11/12/2021

Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium control

Experimental advances enabling high-resolution external control create n...
research
03/07/2022

Reinforcement Learning for Location-Aware Scheduling

Recent techniques in dynamical scheduling and resource management have f...

Please sign up or login with your details

Forgot password? Click here to reset