Title: RILe: Reinforced Imitation Learning
Primary area: reinforcement learning
Keywords: Imitation Learning, Inverse Reinforcement Learning

Abstract: Acquiring complex behaviors is essential for artificially intelligent agents, yet learning these behaviors in high-dimensional settings, like robotics, poses a significant challenge due to the vast search space. There are three main approaches that address this challenge: 1. Reinforcement learning (RL) defines a reward function, which requires extensive manual effort, 2. Inverse reinforcement learning (IRL) uncovers reward functions from expert demonstrations but relies on a computationally expensive iterative process, and 3. Imitation learning (IL) directly compares an agent's actions with expert demonstrations; however, in high-dimensional environments, such binary comparisons often offer insufficient feedback for effective learning. To address the limitations of existing methods, we introduce RILe (Reinforced Imitation Learning), a framework that learns a dense reward function efficiently and achieves strong performance in high-dimensional tasks.
Building on prior methods, RILe combines the granular reward function learning of IRL and computational efficiency of IL.
Specifically, RILe introduces a novel trainer–student framework: the trainer distills an adaptive reward function, and the student uses this reward signal to imitate expert behaviors. Uniquely, the trainer is a reinforcement learning agent that learns a policy for generating rewards. The trainer is trained to select optimal reward signals by distilling signals from a discriminator that judges the student's proximity to expert behavior. We evaluate RILe on general reinforcement learning benchmarks and robotic locomotion tasks, where RILe achieves state-of-the-art performance.