to download the project base paper reinforcement learning project.


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. However, the effect of these design choices on established baselines remains understudied. In this work, we aim to bridge this gap by conducting a retrospective analysis of recent works in offline RL and propose ReBRAC, a minimalistic algorithm that integrates such 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, demonstrating its state-of-the-art performance among ensemble-free methods in both offline and offline-to-online settings. To further illustrate the efficacy of these design choices, we perform a large-scale ablation study and hyperparameter sensitivity analysis on the scale of thousands of experiments.

The interest of the reinforcement learning (RL) community in the offline context has resulted in a plethora of new algorithms specifically designed to learn highly performant policies without the capacity to interact with an environment (Levineetal.,2020; Prudencioetal.,2022). However, as with breakthroughs in onlineRL many of those algorithms come with an extra complexity-design and implementation choices beyond core algorithmic innovations, necessitating a cautious effort in reproduction, hyperparameter adjustment, and causal attribution of performance benefits. Another technique to accelerate neural network convergence is large batch optimization(Youetal.,2017,2019).While studying batch sizes greater than 256 is limited, several previous works have employed them. For example, in Nikulinetal, the convergence of SAC-N was hastened. (2022). More recently proposed algorithms use larger batches for training as well, although without offering extensive assessments.


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