Bounded Risk-Sensitive Markov Game and Its Inverse Reward Learning Problem

3 Sep 2020Ran TianLiting SunMasayoshi Tomizuka

Classical game-theoretic approaches for multi-agent systems in both the forward policy learning/design problem and the inverse reward learning problem often make strong rationality assumptions: agents are perfectly rational expected utility maximizers. Specifically, the agents are risk-neutral to all uncertainties, maximize their expected rewards, and have unlimited computation resources to explore such policies... (read more)

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