OFFLINE Q-LEARNING ON DIVERSE MULTI-TASK DATA BOTH SCALES AND GENERALIZES

Abstract

The potential of offline reinforcement learning (RL) is that high-capacity models trained on large, heterogeneous datasets can lead to agents that generalize broadly, analogously to similar advances in vision and NLP. However, recent works argue that offline RL methods encounter unique challenges to scaling up model capacity. Drawing on the learnings from these works, we re-examine previous design choices and find that with appropriate choices: ResNets, cross-entropy based distributional backups, and feature normalization, offline Q-learning algorithms exhibit strong performance that scales with model capacity. Using multi-task Atari as a testbed for scaling and generalization, we train a single policy on 40 games with near-human performance using up-to 80 million parameter networks, finding that model performance scales favorably with capacity. In contrast to prior work, we extrapolate beyond dataset performance even when trained entirely on a large (400M transitions) but highly suboptimal dataset (51% human-level performance). Compared to return-conditioned supervised approaches, offline Q-learning scales similarly with model capacity and has better performance, especially when the dataset is suboptimal. Finally, we show that offline Q-learning with a diverse dataset is sufficient to learn powerful representations that facilitate rapid transfer to novel games and fast online learning on new variations of a training game, improving over existing state-of-the-art representation learning approaches.

1. INTRODUCTION

High-capacity neural networks trained on large, diverse datasets have led to remarkable models that can solve numerous tasks, rapidly adapt to new tasks, and produce general-purpose representations in NLP and vision (Brown et al., 2020; He et al., 2021) . The promise of offline RL is to leverage these advances to produce polices with broad generalization, emergent capabilities, and performance that exceeds the capabilities demonstrated in the training dataset. Thus far, the only offline RL approaches that demonstrate broadly generalizing policies and transferable representations are heavily-based on supervised learning (Reed et al., 2022; Lee et al., 2022) . However, these approaches are likely to perform poorly when the dataset does not contain expert trajectories (Kumar et al., 2021b) . Offline Q-learning performs well across dataset compositions in a variety of simulated (Gulcehre et al., 2020; Fu et al., 2020) and real-world domains (Chebotar et al., 2021; Soares et al., 2021) , however, these are largely centered around small-scale, single-task problems where broad generalization and learning general-purpose representations is not expected. Scaling these methods up to high-capcity models on large, diverse datasets is the critical challenge. Prior works hint at the difficulties: on small-scale, single-task deep RL benchmarks, scaling model capacity can lead to instabilities or degrade performance (Van Hasselt et al., 2018; Sinha et al., 2020; Ota et al., 2021) explaining why decade-old tiny 3-layer CNN architectures (Mnih et al., 2013) are still prevalent. Moreover, works that have scaled architectures to millions of parameters (Espeholt et al., 2018; Teh et al., 2017; Vinyals et al., 2019; Schrittwieser et al., 2021) typically focus on online learning and employ many sophisticated techniques to stabilize learning, such as supervised auxiliary losses, distillation, and pre-training. Thus, it is unclear whether offline Q-learning can be scaled to high-capacity models.

