Mildly Conservative Q-Learning for Offline Reinforcement Learning

Abstract: Offline reinforcement learning (RL) defines the task of learning from a static logged dataset without continually interacting with the environment. The distribution shift between the learned policy and the behavior policy makes it necessary for the value function to stay conservative such that out-of-distribution (OOD) actions will not be severely overestimated. However, existing approaches, penalizing the unseen actions or regularizing with the behavior policy, are too pessimistic, which suppresses the generalization of the value function and hinders the performance improvement. This paper explores mild but enough conservatism for offline learning while not harming generalization. We propose Mildly Conservative Q-learning (MCQ), where OOD actions are actively trained by assigning them proper pseudo Q values. We theoretically show that MCQ induces a policy that behaves at least as well as the behavior policy and no erroneous overestimation will occur for OOD actions. Experimental results on the D4RL benchmarks demonstrate that MCQ achieves remarkable performance compared with prior work. Furthermore, MCQ shows superior generalization ability when transferring from offline to online, and significantly outperforms baselines. Our code is publicly available at https://github.com/dmksjfl/MCQ.

• The paper introduces MCQ, which employs a Mildly Conservative Bellman operator to balance accurate value estimation and policy generalization in offline RL.
• Theoretical analysis proves that the operator is a contraction in the behavior policy’s support, ensuring policies perform at least as well as the behavior policy.
• Empirical evaluations on the D4RL benchmark show that MCQ outperforms existing methods, enabling robust offline-to-online transitions and improved generalization.

Mildly Conservative $Q$-Learning for Offline Reinforcement Learning

The paper introduces a novel approach to offline reinforcement learning (RL) called Mildly Conservative $Q$-learning (MCQ). The authors focus on addressing the challenges associated with policy learning from a finite dataset without further interaction with the environment. Specifically, they target the issue of distributional shift, where the learned policy might deviate from the behavior policy, leading to significant errors in value estimation for out-of-distribution (OOD) actions. The solution proposed by the authors seeks to find a balance between maintaining conservatism in value estimation and enabling generalization to unseen actions effectively.

Key Contributions

1. Mildly Conservative $Q$-Learning (MCQ): The core contribution of the paper is the development of MCQ, which utilizes a Mildly Conservative Bellman (MCB) operator to perform value updates. Unlike existing approaches that penalize OOD actions harshly or constrain policies to remain close to the behavior policy, MCQ actively assigns pseudo $Q$ values to OOD actions in a more optimistic manner. This allows for well-estimating value functions even in regions not covered by the dataset while ensuring these estimates do not drive policy learning off-course.
2. Theoretical Analysis: The paper provides a rigorous theoretical backing for the MCB operator. The authors demonstrate that this operator is a contraction in the support of the behavior policy. They prove that policies derived using MCQ exhibit performance at least as good as the behavior policy, ensuring it does not suffer from the erroneous value overestimation common in prior methods.
3. Empirical Evaluation: Extensive experiments on the D4RL benchmark demonstrate that MCQ significantly outperforms existing model-free offline RL methods, especially on non-expert datasets that lack comprehensive action coverage. MCQ is shown to provide superior generalization capabilities when operating in the offline-to-online transition, highlighting its practical applicability and robustness.

Implications and Future Directions

The primary implication of this research is the advancement of offline RL by allowing for milder pessimism while still achieving robust policy improvements. The balance achieved in MCQ between conservatism in value estimation and generalization offers a noteworthy improvement over prior methods, which often lock the learned policy to conservative choices, hence limiting performance, especially in non-expert settings.

From a practical standpoint, the approach facilitates better fine-tuning when transitioning from offline to online learning, thereby reducing the dependency on high-quality data for effective training. This adaptability opens the door to wider applications of RL, especially in domains where collecting data is costly or risky.

Future research may explore further optimizations and extensions of the MCB operator to enhance its applicability to even more complex high-dimensional tasks. Additionally, the exploration of hybrid approaches that incorporate mild conservatism within model-based offline RL or hierarchical RL frameworks could potentially yield additional benefits, pushing the boundaries of what's achievable in offline RL scenarios.

In summary, this paper provides a significant contribution to the field of offline reinforcement learning, proposing an innovative and effective method for navigating the intricacies of distribution shift while maintaining accurate action value estimates.