Agent Societies
A Learning Multi-Agent Communication through Structured Attentive Reasoning: Appendix
Recurrent experience replay in distributed reinforcement learning.
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning Yiqin Y ang
Moreover, we extend ICQ to multi-agent tasks by decomposing the joint-policy under the implicit constraint. Experimental results demonstrate that the extrapolation error is successfully controlled within a reasonable range and insensitive to the number of agents.
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems Jiayu Chen
Multi-agent games allow sophisticated interactions between agents and environment. Feasible solutions may require non-trivial intra-agent coordination, which leads to substantially more complex strategies than the single-agent setting.
EpisodicFutureThinkingMechanismforMulti-agent ReinforcementLearning
Onenaiveapproach to incorporate this ability into AI is through future trajectory prediction using model-based RL [40,19,31,54,28,26].
