Hyperparameter Selection for Offline Reinforcement Learning (2007.09055v1)

Abstract: Offline reinforcement learning (RL purely from logged data) is an important avenue for deploying RL techniques in real-world scenarios. However, existing hyperparameter selection methods for offline RL break the offline assumption by evaluating policies corresponding to each hyperparameter setting in the environment. This online execution is often infeasible and hence undermines the main aim of offline RL. Therefore, in this work, we focus on \textit{offline hyperparameter selection}, i.e. methods for choosing the best policy from a set of many policies trained using different hyperparameters, given only logged data. Through large-scale empirical evaluation we show that: 1) offline RL algorithms are not robust to hyperparameter choices, 2) factors such as the offline RL algorithm and method for estimating Q values can have a big impact on hyperparameter selection, and 3) when we control those factors carefully, we can reliably rank policies across hyperparameter choices, and therefore choose policies which are close to the best policy in the set. Overall, our results present an optimistic view that offline hyperparameter selection is within reach, even in challenging tasks with pixel observations, high dimensional action spaces, and long horizon.

• The paper demonstrates that offline evaluation techniques, including FQE, reliably rank policies despite hyperparameter sensitivity in ORL.
• It empirically shows that algorithms like BC, CRR, and D4PG are highly influenced by hyperparameter choices and Q-value estimation methods.
• The findings suggest that calibrated offline ranking methods can attain near-optimal policy performance in complex, high-dimensional tasks.

Overview of Hyperparameter Selection for Offline Reinforcement Learning

The paper "Hyperparameter Selection for Offline Reinforcement Learning" addresses the significant challenge of selecting optimal hyperparameters within the context of Offline Reinforcement Learning (ORL). ORL involves learning a policy from pre-collected, logged data without further interactions with the environment, which is crucial for applying RL techniques to real-world scenarios where data collection is costly or unsafe.

Main Contributions

The paper highlights the intricacies of hyperparameter selection in ORL, an endeavor complicated by the restriction against online evaluation of policies. The standard hyperparameter tuning practice entails evaluating policies online, which infringes upon the offline nature of ORL. Consequently, the authors propose alternative offline methods to rank policies trained with varied hyperparameters using only the logged data.

The authors present a comprehensive empirical paper that reveals:

1. ORL Algorithms' Sensitivity: ORL algorithms exhibit considerable variance in performance based on hyperparameter configurations.
2. Impact of Algorithm and Q-Value Estimation: The choice of ORL algorithm and Q-value estimation method substantially affects hyperparameter selection outcomes.
3. Accuracy in Ranking: Carefully controlling these factors enables reliable ranking of policies across hyperparameter settings, facilitating the selection of near-optimal policies.

Methodology

The paper leverages a variety of ORL methods, primarily focusing on three algorithms: Behavior Cloning (BC), Critic Regularized Regression (CRR), and Distributed Distributional Deep Deterministic Policy Gradient (D4PG). These algorithms were selected to examine the role of hyperparameter configurations in detail.

The approach to hyperparameter selection hinges on offline statistics that summarize policy performance without live environment interactions. The authors utilized both the ORL-generated critic and re-evaluated critics through Fitted Q Evaluation (FQE), assessing their effectiveness using metrics like Spearman's rank correlation and regret @ k.

Numerical Results

Empirical evaluations demonstrate that FQE significantly mitigates the common issue of overestimation associated with ORL methods, especially with D4PG. This finding underscores the potential of FQE as a reliable tool for policy assessment in an offline setting. The paper reports strong rank correlations for BC and CRR across most challenging domains, indicating their robustness to the offline selection process.

However, despite FQE's efficacy, certain tasks, notably those involving high-dimensional action and observation spaces such as DM Locomotion, present continued challenges in achieving precise policy ranks, notably with algorithms prone to greater exploration freedoms, like D4PG.

Implications and Future Directions

The results present an optimistic perspective that precise offline hyperparameter selection is achievable, even for tasks featuring pixel-based observations and extensive action spaces. This contributes substantially to the theoretical understanding and practical implementation of ORL in complex domains.

Moving forward, the work opens avenues for refining FQE's hyperparameter tuning processes and exploring its generalizability across broader contexts. Additionally, advancing model-based or importance-sampling techniques for OPE in similar challenging environments remains a key area for future research.

In conclusion, this paper makes significant strides in adapting hyperparameter selection processes to align with the offline constraints of ORL, thus facilitating the deployment of RL strategies in its intended real-world applications.