Replay across Experiments: A Natural Extension of Off-Policy RL (2311.15951v2)

Abstract: Replaying data is a principal mechanism underlying the stability and data efficiency of off-policy reinforcement learning (RL). We present an effective yet simple framework to extend the use of replays across multiple experiments, minimally adapting the RL workflow for sizeable improvements in controller performance and research iteration times. At its core, Replay Across Experiments (RaE) involves reusing experience from previous experiments to improve exploration and bootstrap learning while reducing required changes to a minimum in comparison to prior work. We empirically show benefits across a number of RL algorithms and challenging control domains spanning both locomotion and manipulation, including hard exploration tasks from egocentric vision. Through comprehensive ablations, we demonstrate robustness to the quality and amount of data available and various hyperparameter choices. Finally, we discuss how our approach can be applied more broadly across research life cycles and can increase resilience by reloading data across random seeds or hyperparameter variations.

• The paper shows that extending experience replay across experiments enhances exploration and reduces convergence time in off-policy RL.
• It integrates seamlessly with algorithms like DMPO, D4PG, and CRR while requiring fewer hyperparameter adjustments.
• Results demonstrate robust gains in control tasks, including locomotion and egocentric vision-based challenges.

Introduction to Replay across Experiments (RaE)

Reinforcement Learning (RL) has made significant advances, demonstrating notable achievements in various domains, particularly those involving control tasks such as locomotion and manipulation. Despite these improvements, RL algorithms often struggle with challenges like high-dimensional observation and action spaces, as well as long training times. A common technique named experience replay has played a pivotal role in enhancing the data efficiency and stability of off-policy RL algorithms. However, traditional replay mechanisms are limited to data collected within a single experiment.

Advancing Off-Policy RL with RaE

Replay across Experiments (RaE) extends the concept of experience replay by incorporating data from past experiments to enrich the learning process of off-policy RL algorithms. By doing so, RaE seeks to enhance exploration, prevent premature convergence of functional approximators, and potentially reduce overall experiment durations. This approach has been shown to yield meaningful improvements across various algorithms and control domains, such as locomotion tasks based on egocentric vision, and demonstrates robustness against the volume and quality of available data.

Emphasizing Simplicity and Efficiency

The main contributions of RaE are characterized by its simplicity and versatility. Not only has it exhibited state-of-the-art performance on multiple challenging domains, but it has also been effectively integrated with several algorithms including DMPO, D4PG, and CRR. A notable aspect is RaE's capacity to outperform existing methods while requiring fewer hyperparameter adjustments, streamlining the integration process into existing workflows.

RaE in Practice

RaE's utility aligns well with the lifecycle of research projects, especially when multiple iterations and adjustments are commonplace. The approach has potential applications in lifelong learning scenarios where preserving and utilizing data from all experimental phases can lead to efficiency gains. Furthermore, RaE demonstrates resilience to changes in project settings, suggesting that it could accommodate evolving experimental conditions while still improving RL agents' performance.

Conclusion

RaE stands out as a minimal yet profound modification to off-policy RL workflows. Its ability to utilize past data equips researchers and practitioners with a straightforward tool for improving the learning process and final performance of RL models. This underscores a shift toward appreciating the role of data management within the experimental iterations of RL research and applications.