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Provably Efficient Learning of Transferable Rewards
Alberto Maria Metelli · Giorgia Ramponi · Alessandro Concetti · Marcello Restelli

Tue Jul 20 06:35 AM -- 06:40 AM (PDT) @

The reward function is widely accepted as a succinct, robust, and transferable representation of a task. Typical approaches, at the basis of Inverse Reinforcement Learning (IRL), leverage on expert demonstrations to recover a reward function. In this paper, we study the theoretical properties of the class of reward functions that are compatible with the expert’s behavior. We analyze how the limited knowledge of the expert’s policy and of the environment affects the reward reconstruction phase. Then, we examine how the error propagates to the learned policy’s performance when transferring the reward function to a different environment. We employ these findings to devise a provably efficient active sampling approach, aware of the need for transferring the reward function, that can be paired with a large variety of IRL algorithms. Finally, we provide numerical simulations on benchmark environments.

Author Information

Alberto Maria Metelli (Politecnico di Milano)
Giorgia Ramponi (Politecnico di Milano)
Alessandro Concetti (Politecnico di Milano)
Marcello Restelli (Politecnico di Milano)

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