Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning (2303.05479v4)

Abstract: A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities. Our approach, calibrated Q-learning (Cal-QL), accomplishes this by learning a conservative value function initialization that underestimates the value of the learned policy from offline data, while also being calibrated, in the sense that the learned Q-values are at a reasonable scale. We refer to this property as calibration, and define it formally as providing a lower bound on the true value function of the learned policy and an upper bound on the value of some other (suboptimal) reference policy, which may simply be the behavior policy. We show that offline RL algorithms that learn such calibrated value functions lead to effective online fine-tuning, enabling us to take the benefits of offline initializations in online fine-tuning. In practice, Cal-QL can be implemented on top of the conservative Q learning (CQL) for offline RL within a one-line code change. Empirically, Cal-QL outperforms state-of-the-art methods on 9/11 fine-tuning benchmark tasks that we study in this paper. Code and video are available at https://nakamotoo.github.io/Cal-QL

Overview of "Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning"

The paper presents a method for offline reinforcement learning (RL) called Calibrated Q-learning (Cal-QL) aimed at improving the efficiency of online fine-tuning. The central proposition of this research is that existing offline RL methods often struggle with fine-tuning due to overly conservative value function estimates. These methods suffer from delayed improvement or even regression in performance when transitioning to online refinement. By proposing a calibrated approach, the authors demonstrate enhanced efficiency in online learning phases.

Calibrated Q-learning addresses an essential challenge in offline RL: integrating the strengths of offline initialization with the flexibility and rapid learning capacity of online interactions. Cal-QL achieves this by learning conservative value function initializations and manipulating these estimates to be "calibrated" against a reference policy. Specifically, the algorithm ensures that learned Q-values do not underestimate baseline conditions while maintaining a conservative stance that avoids overestimation—a common pitfall in offline RL fine-tuning settings.

Contributions and Methodology

1. Challenge of Value Estimation: The paper identifies the challenge of learned Q-values during fine-tuning. When conservative methods carry these Q-values forward without alignment to any reference scale, a mismatch can emerge, leading to performance dips at the onset of online interaction.
2. Calibration Strategy: Cal-QL addresses the misalignment by calibrating the Q-function with respect to a reliable reference value. This calibration operates by minimizing the gap between the learned Q-value's expectation and a known policy's value function baseline. By ensuring that the learning process neither overestimates nor drastically underestimates, Cal-QL improves the stability and efficacy of subsequent online tuning.
3. Implementation on Existing Algorithms: The technique is demonstrated as a simple addition to existing methods, specifically Conservative Q-learning (CQL). The calibration adjustment involves integrating a minimal code modification, showcasing the effortlessness with which existing conservative methods can be augmented to improve performance in online settings.
4. Theoretical Foundations: A detailed theoretical exploration provides bounds on the cumulative regret, demonstrating the improved potential of Cal-QL in terms of learning efficiency. This analysis connects dynamic adjustments made during fine-tuning and their implications on expected policy performance in comparison to baseline strategies.

Empirical Results

Empirically, Cal-QL shows compelling improvements over state-of-the-art methods across benchmark tasks, particularly promoting rapid achievement of high performance following offline initialization. In environments like robotic manipulation and maze navigation, Cal-QL demonstrates higher asymptotic returns and lower cumulative regret during online learning stages. The paper systematically compares against other models, revealing distinct advantages in early-phase learning and overall policy effectiveness.

Implications and Future Directions

The success of Cal-QL suggests promising developments for offline RL methods, urging a reconsideration of fine-tuning strategies practiced widely. By reducing the calibration problem, the work accentuates how nuanced adjustments can reduce sample complexity and accelerate learning.

The research lays a foundation for further exploration into scale calibration within RL models, potentially influencing how offline policy initializations are approached in diverse applications, including robotics, autonomous driving, and game playing. Future investigations could extend the method to handle broader applicability or adapt its framework to non-stationary environments and tasks with more complex objectives.

Overall, "Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning" significantly contributes to RL literature, promoting a nuanced understanding of fine-tuning dynamics and setting a new standard for offline-to-online RL methodology.