Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning
This work addresses computational efficiency for offline RL practitioners, offering an incremental improvement by balancing policy constraints and ensemble size.
The paper tackles the computational overhead of large ensembles in offline reinforcement learning by incorporating behavioral cloning into policy updates, achieving state-of-the-art performance with smaller ensembles and enabling stable online fine-tuning.
Offline reinforcement learning agents seek optimal policies from fixed data sets. With environmental interaction prohibited, agents face significant challenges in preventing errors in value estimates from compounding and subsequently causing the learning process to collapse. Uncertainty estimation using ensembles compensates for this by penalising high-variance value estimates, allowing agents to learn robust policies based on data-driven actions. However, the requirement for large ensembles to facilitate sufficient penalisation results in significant computational overhead. In this work, we examine the role of policy constraints as a mechanism for regulating uncertainty, and the corresponding balance between level of constraint and ensemble size. By incorporating behavioural cloning into policy updates, we show empirically that sufficient penalisation can be achieved with a much smaller ensemble size, substantially reducing computational demand while retaining state-of-the-art performance on benchmarking tasks. Furthermore, we show how such an approach can facilitate stable online fine tuning, allowing for continued policy improvement while avoiding severe performance drops.