most citedExplaining Reinforcement Learning Policies through Counterfactual Trajectories

3 citations · 3 across the 1 of their papers we have counts for

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cs.LG2022★ 3 cited

Explaining Reinforcement Learning Policies through Counterfactual Trajectories

Julius Frost, Olivia Watkins, Eric Weiner +4

In order for humans to confidently decide where to employ RL agents for real-world tasks, a human developer must validate that the agent will perform well at test-time. Some policy…