MA TREX: Mutli agent Trajectory Ranked Reward Extrapolation via Inverse Reinforcement Learning

Sili Huang
Sili Huang
Bo Yang
Bo Yang
Haiyin Piao
Haiyin Piao
Zhixiao Sun
Zhixiao Sun
Yi Chang
Yi Chang

International Conference on Knowledge Science, Engineering and Management, pp. 3-14, 2020.

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Abstract:

Trajectory-ranked reward extrapolation (T-REX) provides a general framework to infer users’ intentions from sub-optimal demonstrations. However, it becomes inflexible when encountering multi-agent scenarios, due to its high complexity caused by rational behaviors, e.g., cooperation and communication. In this paper, we propose a novel Mult...More

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