Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Multiagent Systems | Computer science,Markov chain,Markov decision process,Artificial intelligence,Generative grammar,Management science,Reinforcement learning |
DocType | Volume | Citations |
Journal | abs/1810.09206 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heechang Ryu | 1 | 1 | 1.36 |
| Hayong Shin | 2 | 126 | 17.25 |
| Jinkyoo Park | 3 | 12 | 7.83 |