Offline RL Without Off-Policy Evaluation (2106.08909v3)

Abstract: Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-critic approach involving off-policy evaluation. In this paper we show that simply doing one step of constrained/regularized policy improvement using an on-policy Q estimate of the behavior policy performs surprisingly well. This one-step algorithm beats the previously reported results of iterative algorithms on a large portion of the D4RL benchmark. The one-step baseline achieves this strong performance while being notably simpler and more robust to hyperparameters than previously proposed iterative algorithms. We argue that the relatively poor performance of iterative approaches is a result of the high variance inherent in doing off-policy evaluation and magnified by the repeated optimization of policies against those estimates. In addition, we hypothesize that the strong performance of the one-step algorithm is due to a combination of favorable structure in the environment and behavior policy.

Insights on "Offline RL Without Off-Policy Evaluation"

The paper "Offline RL Without Off-Policy Evaluation" presents an intriguing approach to offline reinforcement learning (RL) that circumvents the prevalent use of off-policy evaluation (OPE). The authors challenge the dominant iterative actor-critic paradigm by suggesting that a one-step constrained policy improvement utilizing on-policy Q estimates of the behavior policy can yield superior outcomes on a significant portion of the D4RL benchmark suite. This proposition challenges the current methodologies and proposes a simple yet effective baseline for offline RL algorithms.

Key Contributions

1. One-step Policy Improvement: The paper demonstrates that a one-step policy improvement using the on-policy Q function estimate often surpasses the performance of iterative algorithms previously hailed in the literature. This result is achieved with reduced computational complexity and better robustness to varying hyperparameter settings.
2. Analysis of Iterative Algorithm Failures: The authors delve into understanding why iterative algorithms often underperform. They identify two critical issues: high variance resulting from off-policy evaluation errors and the exploitation of these errors through repeated optimization steps. This insight highlights fundamental limitations inherent in iterative approaches that attempt to infer Q values outside the behavior policy's coverage.
3. Practical Guidance: While the one-step algorithm proves to be a robust baseline, the paper acknowledges scenarios where iterative algorithms may outperform, particularly when the dataset is large and the behavior policy sufficiently covers the state-action space.

Notable Results

The one-step algorithm markedly outperformed the results of existing iterative methods on various tasks within the D4RL benchmark, including significant advances in gym-mujoco and Adroit environments. This baseline introduces a paradigm shift by achieving these gains without intricate algorithmic modifications such as ensemble methods or regularization in Q-function evaluation.

Theoretical and Empirical Implications

From a theoretical standpoint, the paper challenges existing assumptions about the necessity of iterative refinement in RL, opening up potential research avenues to explore other one-step or constrained methods. Empirically, the findings suggest that simple approaches might suffice in practical deployment scenarios where computational resources or expert tuning is limited.

Areas for Future Work

Future studies could benefit from extending this line of inquiry by formalizing theoretical guarantees for when one-step approaches consistently outperform iterative counterparts. Moreover, exploring hybrid models that can dynamically switch between one-step and iterative approaches based on dataset characteristics or learning phase might optimize performance in a broader range of applications.

In conclusion, "Offline RL Without Off-Policy Evaluation" stands as a thought-provoking contribution to RL literature, proposing a fundamental reevaluation of how offline policies are optimized and evaluated. By simplifying the process and highlighting scenarios where traditional methods falter, it lays the groundwork for developing both robust and scalable RL applications in diverse real-world settings.