DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections (1906.04733v2)

Abstract: In many real-world reinforcement learning applications, access to the environment is limited to a fixed dataset, instead of direct (online) interaction with the environment. When using this data for either evaluation or training of a new policy, accurate estimates of discounted stationary distribution ratios -- correction terms which quantify the likelihood that the new policy will experience a certain state-action pair normalized by the probability with which the state-action pair appears in the dataset -- can improve accuracy and performance. In this work, we propose an algorithm, DualDICE, for estimating these quantities. In contrast to previous approaches, our algorithm is agnostic to knowledge of the behavior policy (or policies) used to generate the dataset. Furthermore, it eschews any direct use of importance weights, thus avoiding potential optimization instabilities endemic of previous methods. In addition to providing theoretical guarantees, we present an empirical study of our algorithm applied to off-policy policy evaluation and find that our algorithm significantly improves accuracy compared to existing techniques.

• The paper introduces DualDICE, which robustly estimates discounted stationary distribution corrections without requiring behavior policy information.
• It avoids high variance issues by eliminating per-step importance weights, thereby enhancing stability and accuracy in off-policy evaluations.
• DualDICE offers theoretical convergence guarantees and shows superior empirical performance, paving the way for safer and more reliable RL applications.

An Expert Review of "DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections"

The paper "DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections" addresses the challenge of estimating stationary distribution corrections in reinforcement learning (RL) when only off-policy data is available. This is crucial for applications where direct interaction with the environment is either infeasible or too risky, such as in healthcare, recommendations, and educational contexts.

Overview

The core contribution of this research is the formulation and development of the DualDICE algorithm. This method focuses on estimating the discounted stationary distribution corrections—a critical component for evaluating and improving policies using offline data. Unlike traditional approaches, DualDICE is designed to be robust and independent of the behavior policy used to generate the data, a feature described as "behavior-agnostic."

DualDICE stands apart by avoiding the direct use of importance weights, which are typically prone to high variance and can destabilize optimization processes. The proposed algorithm introduces a novel dual formulation, leveraging Fenchel duality and changes of variables to optimize the stationarity distribution ratios efficiently.

Key Findings

1. Behavior-Agnostic Estimation: DualDICE effectively estimates the stationary distribution corrections without assuming knowledge of the behavior policy, broadening its applicability.
2. Avoidance of High Variance: By circumventing the per-step importance weights, DualDICE addresses the variance problems that plague other algorithms. The avoidance of these weights mitigates optimization instability, enhancing the robustness of the algorithm.
3. Theoretical Guarantees: The paper provides detailed theoretical analyses, establishing convergence guarantees for the DualDICE algorithm. The analysis outlines bounds on the mean squared error of the policy evaluation, considering various sources of potential error including statistical error, approximation error, and optimization error.
4. Empirical Performance: Through a series of experiments comparing DualDICE with existing methods like TD-based approaches and importance sampling, the paper demonstrates the superior accuracy and stability of DualDICE, particularly in complex environments with hard-to-simulate dynamics.

Implications

DualDICE's design suggests significant implications for both theoretical paper and practical application of RL systems:

• Theoretical Advancements: DualDICE's novel approach using duality and reformulation offers a new perspective on addressing distribution corrections, which could inspire further research into optimization methods for RL under constrained and uncertain data conditions.
• Practical Applications: By allowing for accurate policy evaluation and improvement using only offline data, DualDICE has practical relevance for sectors where real-time interactions are constrained. This could accelerate the deployment and safety assurance of RL in critical areas like autonomous driving, healthcare policy design, and more.

Future Directions

The paper hints at several avenues for future research:

• Integration into Off-Policy Training: Incorporating DualDICE during the phase of policy improvement could yield an end-to-end off-policy RL training framework.
• Further Analysis of Convex Functions: Exploring different convex penalty functions within DualDICE could optimize the trade-offs between variance and bias further, potentially improving estimation precision.
• Real-World Deployment and Testing: While the empirical trials on simulated control tasks are promising, testing DualDICE in real-world environments could validate its effectiveness further and highlight areas for modification.

In conclusion, DualDICE marks a substantial step forward in the field of reinforcement learning, addressing long-standing issues with off-policy evaluation and policy improvement. Its development and results pave the way for more dependable RL system applications across diverse fields, especially those reliant on offline datasets.