Click here to download the project base paper reinforcement learning project.
Abstract:
Recent years have witnessed significant advancements in deep learning projects in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity. While these algorithms have led to noteworthy improvements, many incorporate seemingly minor design choices that impact their effectiveness beyond core algorithmic advances.
In this work, we address this gap by conducting a retrospective analysis of recent offline RL methods. We introduce ReBRAC, a minimalistic algorithm that incorporates these design elements, built on top of the TD3+BC method. We evaluate ReBRAC on 51 datasets with both proprioceptive and visual state spaces using D4RL and V-D4RL benchmarks. Our results demonstrate ReBRAC’s state-of-the-art performance among ensemble-free methods in both offline and offline-to-online settings. To further highlight the importance of these design choices, we conduct a large-scale ablation study and hyperparameter sensitivity analysis across thousands of experiments.
The RL community’s growing interest in the offline context has led to a surge of algorithms aimed at learning high-performance policies without interacting with an environment (Levine et al., 2020; Prudencio et al., 2022). However, similar to breakthroughs in online RL, many of these algorithms include additional complexities in design and implementation beyond core innovations. This complexity demands careful reproduction, hyperparameter tuning, and a clear understanding of the factors driving performance gains. Another technique for speeding up neural network convergence is large batch optimization (You et al., 2017, 2019). Although studies on batch sizes larger than 256 are limited, previous works like Nikulin et al. (2022) have accelerated SAC-N’s convergence using this approach. More recently, newer algorithms have also adopted larger batches, although they lack extensive assessments.
