most citedIntegrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents

11 citations · 11 across the 2 of their papers we have counts for

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2 papers

cs.AI2023

Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes

Yotam Amitai, Yael Septon, Ofra Amir

Explainable reinforcement learning (XRL) methods aim to help elucidate agent policies and decision-making processes. The majority of XRL approaches focus on local explanations, see…

cs.LG2022★ 11 cited

Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents

Yael Septon, Tobias Huber, Elisabeth André +1

Explaining the behavior of reinforcement learning agents operating in sequential decision-making settings is challenging, as their behavior is affected by a dynamic environment and…