The replicability crisis in the social, behavioral, and data sciences has led to the formulation of algorithm frameworks for replicability -- i.e., a requirement that an algorithm produce identical outputs (with high probability) when run on two

Imitation learning methods seek to learn from an expert either through behavioral cloning (BC) for the policy or inverse reinforcement learning (IRL) for the reward. Such methods enable agents to learn complex tasks from humans that are difficult to capture with hand-designed reward functions.

Through numerical experiments, we demonstrate the effectiveness and advantages of our approach.

In Distributional Reinforcement Learning (D-RL) [Bellemare et al., 2023], an agent aims to estimate Sutton and Barto, 2018], where the objective is to predict the expected return only. In Section 3, we answer this methodological question, showing that it is possible to reformulate Policy Evaluation in a distributional setting so that its performance index is explicitly intertwined with the representation of the (state or action) spaces.