… tractable solutions for regularized IRL problems that can be derived from policy regularization
and its … We evaluate our Regularized Adversarial Inverse Reinforcement Learning (RAIRL) …

… analyse the inverse reinforcement learning problem, … regularized version of the Markov
decision process (MDP), which provides a better-posed setting for inverse reinforcement learning…

… Inverse Reinforcement Learning (IRL) techniques have been … solve the problems of classical
Imitation Learning (IL) methods (Ng … of IQ-Learn is not consistent with regularization on the …

Inverse reinforcement learning (IRL) aims to infer a reward from expert demonstrations,
motivated by the idea that the reward, rather than the policy, is the most succinct and transferable …

… Sparse markov decision processes with causal sparse tsallis entropy regularization for
reinforcement learning. IEEE Robotics and Automation Letters, 3(3):1466–1473, 2018b. …

… Entropy regularization: We use an entropy regularizer weight of 0.1 for Ant, Swimmer, and
HalfCheetah across all methods. We use an entropy regularizer weight of 1.0 on the point …

… to the conventional regularization methods of Tikhonov regularization and … reinforcement
learning (RL) algorithm to search for an iterative method to solve the following general inverse …

… , inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal.
This work proposes a model-free algorithm to solve the entropy-regularized IRL problem. …

… Learning (ML) and optimization taking into account the optimal manners. This paper reviews
the history of the IOC and Inverse Reinforcement Learning (IRL) … Regularization has been …

… Inverse reinforcement learning (IRL) is the problem of inferring … functions with smaller values
as a form of regularization. … side of the margin augmented by a regularizing loss term l i T ψ …