Reward-free World Models for Online Imitation Learning

Imitation learning (IL) enables agents to acquire skills directly from expert demonstrations, providing a compelling alternative to reinforcement learning. However, prior online IL approaches struggle with complex tasks characterized by high-dimensional inputs and complex dynamics. In this work, we propose a novel approach to online imitation learning that leverages reward-free world models. Our method learns environmental dynamics entirely in latent spaces without reconstruction, enabling efficient and accurate modeling. We adopt the inverse soft-Q learning objective, reformulating the optimization process in the Q-policy space to mitigate the instability associated with traditional optimization in the reward-policy space. By employing a learned latent dynamics model and planning for control, our approach consistently achieves stable, expert-level performance in tasks with high-dimensional observation or action spaces and intricate dynamics. We evaluate our method on a diverse set of benchmarks, including DMControl, MyoSuite, and ManiSkill2, demonstrating superior empirical performance compared to existing approaches. Imitation learning (IL) has garnered considerable attention due to its broad applications across various domains, such as robotic manipulation (Zhu et al., 2023; Chi et al., 2023) and autonomous driving (Hu et al., 2022; Zhou et al., 2021). Unlike reinforcement learning, where agents learn through reward signals, IL involves learning directly from expert demonstrations.