Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data
Ran Tian,Masayoshi Tomizuka,Liting Sun,Ran Tian,Masayoshi Tomizuka,Liting Sun
Reward function, as an incentive representation that recognizes humans’ agency and rationalizes humans’ actions, is particularly appealing for modeling human behavior in human-robot interaction. Inverse Reinforcement Learning is an effective way to retrieve reward functions from demonstrations. However, it has always been challenging when applying it to multi-agent settings since the mutual influe...
