Inverse reinforcement learning attempts to reconstruct the reward function in a Markov
decision problem, using observations of agent actions. As already observed in Russell …

Two main challenges in Reinforcement Learning (RL) are designing appropriate reward
functions and ensuring the safety of the learned policy. To address these challenges, we …

Inverse optimal control, also known as inverse reinforcement learning, is the problem of
recovering an unknown reward function in a Markov decision process from expert …

While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on
estimating a reward function that best explains an expert agent's policy or demonstrated …

Inverse reinforcement learning (IRL) denotes a powerful family of algorithms for recovering a
reward function justifying the behavior demonstrated by an expert agent. A well-known …

Inverse reinforcement learning (IRL) aims to recover the reward function and the associated
optimal policy that best fits observed sequences of states and actions implemented by an …

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 …

In the field of reinforcement learning there has been recent progress towards safety and high-
confidence bounds on policy performance. However, to our knowledge, no practical …

In this paper, we propose a novel setting for Inverse Reinforcement Learning (IRL), namely"
Learning from a Learner"(LfL). As opposed to standard IRL, it does not consist in learning a …

Abstract Popular Maximum Entropy Inverse Reinforcement Learning approaches require the
computation of expected state visitation frequencies for the optimal policy under an estimate …