Agents
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
Sai Qian Zhang, Qi Zhang, Jieyu Lin
In the research literature, there have been three lines of research that try to mitigate the instability and inefficiency caused by decentralized execution.
CoordinatedProximalPolicyOptimization
Insuch applications, a team of agents aim to maximize a joint expected utility through a single global reward.
Discovery of Useful Questions as Auxiliary Tasks
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado P. van Hasselt, David Silver, Satinder Singh
Neural Information Processing Systems http://nips.cc/
Model-Based Opponent Modeling Xiaopeng Y u Jiechuan Jiang Wanpeng Zhang Haobin Jiang Zongqing Lu School of Computer Science, Peking University
When one agent interacts with a multi-agent environment, it is challenging to deal with various opponents unseen before.
b39fcf2e88dad4c38386b3af6edf88c7-Paper-Conference.pdf
We propose to view the outcomes of the agents' dynamics as inducing a "meta-game" between the users.