Contrastive Example-Based Control
Kyle Hatch Benjamin Eysenbach Rafael Rafailov Tianhe Yu Ruslan Salakhutdinov Sergey Levine Chelsea Finn
While many real-world problems that might benefit from reinforcement
learning, these problems rarely fit into the MDP mold: interacting with the
environment is often expensive and specifying reward functions is challenging.
Motivated by these challenges, prior work has developed data-driven approaches
that learn entirely from samples from the transition dynamics and examples of
high-return states. These methods typically learn a reward function from
high-return states, use that reward function to label the transitions, and then
apply an offline RL algorithm to these transitions. While these methods can
achieve good results on many tasks, they can be complex, often requiring
regularization and temporal difference updates. In this paper, we propose a
method for offline, example-based control that learns an implicit model of
multi-step transitions, rather than a reward function. We show that this
implicit model can represent the Q-values for the example-based control
problem. Across a range of state-based and image-based offline control tasks,
our method outperforms baselines that use learned reward functions; additional
experiments demonstrate improved robustness and scaling with dataset size.