RvS: What is Essential for Offline RL via Supervised Learning? (2112.10751v2)

Abstract: 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.

An Examination of Supervised Learning as a Template for Offline Reinforcement Learning

The paper "RvS: What is Essential for Offline RL via Supervised Learning?" addresses a salient topic in the field of machine learning: the use of supervised learning in offline reinforcement learning (RL). The work critically evaluates the conditions under which supervised learning methods can be effectively utilized as an alternative to traditional techniques reliant on temporal difference (TD) learning.

At its core, the paper investigates the adequacy of a basic architecture—a two-layer feedforward multilayer perceptron (MLP)—and explores how this framework can achieve results on par with more complex methods leveraging TD learning or Transformer models in offline RL contexts. This analysis is conducted across a diverse set of environments. The assertion is that maximizing likelihood via this simple architecture competes favorably against established, complex algorithms.

One of the principal insights from the paper is the paramount importance of model capacity, which is influenced by both the choice of regularization and network architecture. These factors are found to significantly impact the performance of supervised learning methods within offline RL. Additionally, the decision of what contextual information to condition actions on—goals versus rewards—dramatically alters outcome efficacies across tasks. This indicates that the optimal approach to designing RL systems via supervised learning may indeed fluctuate based on the specific characteristics of the environments and data at hand.

The implications of these findings are wide-reaching. For practitioners, this paper provides a streamlined "field guide" for implementing reinforcement learning via supervised means, advocating for a model design that is simultaneously simple yet reflective of careful choice in architecture and conditioning strategy. The research elucidates potential weak spots, particularly on datasets populated with random data where existing RvS methods show limitations in aligning performance with more traditional RL paradigms.

This paper's results are a call to action for refining the mechanics of outcome conditioning within supervised learning applied to RL. The evidence underscores that while high-capacity models can yield excellent performance, there remains an overarching need for methodological improvements, especially in addressing datasets that currently resist such approaches.

Moving forward, the exploration of automated tuning of parameters such as network capacity and regularization, as well as a deeper examination of condition parameter selection (whether rewards or goals), merit significant attention. Moreover, this paper implicitly encourages the RL community to disentangle the complexities associated with different types of RL environments—specially identified as those with intricate subtrajectory stitching challenges—and develop nuanced solutions tailored to these diverse problem spaces.

By synthesizing these findings, the paper sets a foundation for future inquiries into reinforcement learning via supervised techniques, highlighting both the potential and the challenges that lie ahead. The research notably redefines what architectural simplicity can achieve in offline RL, all while probing the boundaries of RvS methods and their application in a rapidly evolving field.