Deep Inverse Reinforcement Learning with Adversarial One-Class Classification

Abstract

Recently, inverse reinforcement learning, which estimates the reward from an expert's trajectories, has been attracting attention for imitating complex behaviors and estimating intentions. This study proposes a novel deep inverse reinforcement learning method that combines LogReg-IRL, an IRL method based on linearly solvable Markov decision process, and ALOCC, an adversarial one-class classification method. The proposed method can quickly learn rewards and state values without reinforcement learning executions or trajectories to be compared. We show that the proposed method obtains a more expert-like gait than LogReg-IRL in the BipedalWalker task through computer experiments.