Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
This addresses the problem of stable and efficient imitation learning for AI agents, though it appears incremental as it builds on existing reinforcement learning frameworks.
The paper tackles imitation learning from limited expert trajectories by estimating the expert policy's support to compute a fixed reward function, reframing it as standard reinforcement learning, and achieves comparable or better performance than state-of-the-art methods in discrete and continuous domains.
We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals. The classical approach of extracting the expert's reward function via inverse reinforcement learning, followed by reinforcement learning is indirect and may be computationally expensive. Recent generative adversarial methods based on matching the policy distribution between the expert and the agent could be unstable during training. We propose a new framework for imitation learning by estimating the support of the expert policy to compute a fixed reward function, which allows us to re-frame imitation learning within the standard reinforcement learning setting. We demonstrate the efficacy of our reward function on both discrete and continuous domains, achieving comparable or better performance than the state of the art under different reinforcement learning algorithms.