Diffusion-Reward Adversarial Imitation Learning

Imitation learning aims to learn a policy from observing expert demonstrations without access to reward signals from environments. Generative adversarial imitation learning (GAIL) formulates imitation learning as adversarial learning, employing a generator policy learning to imitate expert behaviors and discriminator learning to distinguish the expert demonstrations from agent trajectories. Despite its encouraging results, GAIL training is often brittle and unstable. Inspired by the recent dominance of diffusion models in generative modeling, this work proposes Diffusion-Reward Adversarial Imitation Learning (DRAIL), which integrates a diffusion model into GAIL, aiming to yield more precise and smoother rewards for policy learning. Specifically, we propose a diffusion discriminative classifier to construct an enhanced discriminator; then, we design diffusion rewards based on the classifier's output for policy learning. We conduct extensive experiments in navigation, manipulation, and locomotion, verifying DRAIL's effectiveness compared to prior imitation learning methods. Moreover, additional experimental results demonstrate the generalizability and data efficiency of DRAIL. Visualized learned reward functions of GAIL and DRAIL suggest that DRAIL can produce more precise and smoother rewards.

Diffusion Model-Augmented Behavioral Cloning

Imitation learning addresses the challenge of learning by observing an expert's demonstrations without access to reward signals from the environment. Behavioral cloning (BC) formulates imitation learning as a supervised learning problem and learns from sampled state-action pairs. Despite its simplicity, it often fails to capture the temporal structure of the task and the global information of expert demonstrations. This work aims to augment BC by employing diffusion models for modeling expert behaviors, and designing a learning objective that leverages learned diffusion models to guide policy learning. To this end, we propose diffusion model-augmented behavioral cloning (Diffusion-BC) that combines our proposed diffusion model guided learning objective with the BC objective, which complements each other. Our proposed method outperforms baselines or achieves competitive performance in various continuous control domains, including navigation, robot arm manipulation, and locomotion. Ablation studies justify our design choices and investigate the effect of balancing the BC and our proposed diffusion model objective.