Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis

ICLR 2019 Avraham RudermanRichard EverettBristy SikderHubert SoyerJonathan UesatoAnanya KumarCharlie BeattiePushmeet Kohli

Reinforcement learning agents are typically trained and evaluated according to their performance averaged over some distribution of environment settings. But does the distribution over environment settings contain important biases, and do these lead to agents that fail in certain cases despite high average-case performance?.. (read more)

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