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Found 2 papers, 1 papers with code
Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes
Explainable reinforcement learning (XRL) methods aim to help elucidate agent policies and decision-making processes.
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
Methods that help users understand the behavior of such agents can roughly be divided into local explanations that analyze specific decisions of the agents and global explanations that convey the general strategy of the agents.
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