- The paper demonstrates that extending the Doubly Robust estimator to sequential decision problems reduces bias and variance in policy evaluation.
- It combines regression-based and importance sampling techniques to effectively manage distribution mismatches between behavior and target policies.
- Empirical results on benchmarks like Mountain Car and the KDD Cup 1998 Donation dataset confirm its superior performance over traditional methods.
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
The paper addresses the challenge of off-policy value evaluation in reinforcement learning (RL), a pertinent issue when estimating the value of a policy using data generated from a different policy. This scenario is common in practical RL applications where direct policy evaluation through deployment can be infeasible due to associated risks or costs.
Traditional approaches either model the Markov Decision Process (MDP) via regression or utilize Importance Sampling (IS) to account for distribution shifts between behavior and target policies. However, these techniques suffer from high variance or bias challenges. The proposed method extends the Doubly Robust (DR) estimator, originally developed for contextual bandits, to sequential decision problems, aiming to mitigate these issues.
Methodology
The DR estimator is designed to handle uncertainty in both the modeling and sampling processes, combining the advantages of regression-based (lower variance) and IS-based (no bias) approaches. The authors propose a simple recursive form for the estimator addressing the inherent distributional mismatch in policy evaluation.
Crucially, the paper analyzes the variance properties of the DR estimator, demonstrating its statistical benefits over traditional methods. DR's variance closely aligns with the Cramer-Rao lower bound under certain conditions, indicating its efficiency. This property ensures that the DR estimator optimally balances bias and variance given the correct model specification.
Strong Numerical Results
Empirical validation on various benchmark problems, such as Mountain Car and Sailing, showcases the DR estimator's superior accuracy compared to other methods. In these tasks, the DR estimator outperforms IS and WIS, notably when the behavior policy diverges from the target policy. Additionally, the inclusion of simulated experiments on the KDD Cup 1998 Donation dataset further exhibits its practical benefits.
Theoretical and Practical Implications
The research underscores the inherent difficulty in off-policy evaluation, laying out the theoretical bounds on estimation accuracy. The DR estimator presented offers a compelling method for reducing variance without increasing bias, especially in complex MDPs where transition dynamics can be reliably estimated.
From a practical standpoint, the ability to perform accurate off-policy evaluation allows safer and more reliable policy improvements in real-world applications. This advantage translates to increased trust in deploying learned policies in scenarios like medical treatment plans or robotics, where decision errors can be costly.
Future Directions
The DR estimator's application to broader RL problems and its integration with modern RL algorithms, such as deep reinforcement learning, could provide further advancements in the field. Exploration into how DR can be practically integrated into existing RL workflows could enhance the robustness of decision-making systems in data-scarce environments.
In conclusion, this work provides a substantial contribution to the field of off-policy RL by introducing a method that effectively balances bias and variance, positioning it as a robust choice for policy evaluation and improvement in both academic and industrial settings.