RvS: What is Essential for Offline RL via Supervised Learning?

Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL. When does this hold true, and which algorithmic components are necessary? Through extensive experiments, we boil supervised learning for offline RL down to its essential elements. In every environment suite we consider, simply maximizing likelihood with a two-layer feedforward MLP is competitive with state-of-the-art results of substantially more complex methods based on TD learning or sequence modeling with Transformers. Carefully choosing model capacity (e.g., via regularization or architecture) and choosing which information to condition on (e.g., goals or rewards) are critical for performance. These insights serve as a field guide for practitioners doing Reinforcement Learning via Supervised Learning (which we coin RvS learning). They also probe the limits of existing RvS methods, which are comparatively weak on random data, and suggest a number of open problems. Offline and off-policy reinforcement learning (RL) are typically addressed using value-based methods. While theoretically appealing because they include performance guarantees under certain assumptions [27], such methods can be difficult to apply in practice; they tend to require complex tricks to stabilize learning and delicate tuning of many hyperparameters. Recent work has explored an alternative approach: convert the RL problem into a conditional, filtered, or weighted imitation learning problem.