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cs.AI2023
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
Jasmina Gajcin, James McCarthy, Rahul Nair +3
A well-defined reward function is crucial for successful training of an reinforcement learning (RL) agent. However, defining a suitable reward function is a notoriously challenging…
cs.AI2023
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning
Jasmina Gajcin, Ivana Dusparic
While reinforcement learning (RL) algorithms have been successfully applied to numerous tasks, their reliance on neural networks makes their behavior difficult to understand and tr…