  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.
