What Matters for Batch Online Reinforcement Learning in Robotics? (2505.08078v1)

Abstract: The ability to learn from large batches of autonomously collected data for policy improvement -- a paradigm we refer to as batch online reinforcement learning -- holds the promise of enabling truly scalable robot learning by significantly reducing the need for human effort of data collection while getting benefits from self-improvement. Yet, despite the promise of this paradigm, it remains challenging to achieve due to algorithms not being able to learn effectively from the autonomous data. For example, prior works have applied imitation learning and filtered imitation learning methods to the batch online RL problem, but these algorithms often fail to efficiently improve from the autonomously collected data or converge quickly to a suboptimal point. This raises the question of what matters for effective batch online RL in robotics. Motivated by this question, we perform a systematic empirical study of three axes -- (i) algorithm class, (ii) policy extraction methods, and (iii) policy expressivity -- and analyze how these axes affect performance and scaling with the amount of autonomous data. Through our analysis, we make several observations. First, we observe that the use of Q-functions to guide batch online RL significantly improves performance over imitation-based methods. Building on this, we show that an implicit method of policy extraction -- via choosing the best action in the distribution of the policy -- is necessary over traditional policy extraction methods from offline RL. Next, we show that an expressive policy class is preferred over less expressive policy classes. Based on this analysis, we propose a general recipe for effective batch online RL. We then show a simple addition to the recipe of using temporally-correlated noise to obtain more diversity results in further performance gains. Our recipe obtains significantly better performance and scaling compared to prior methods.

Analyzing Key Components of Batch Online Reinforcement Learning in Robotics

The paper under review provides a detailed empirical investigation into batch online reinforcement learning (RL) within the robotics domain, aiming to discern effective methodologies for utilizing autonomously collected data to enhance policy performance. This approach diverges from traditional RL paradigms by enabling offline training on data amassed during policy deployment, and presents potential advantages in scalability and reduction of manual data collection efforts. Despite the inherent promise of batch online RL, existing methodologies frequently confront challenges related to suboptimal convergence and inefficiency in leveraging collected data. The authors examine three pivotal axes—algorithm class, policy extraction methods, and policy expressivity—to establish a general recipe for maximizing batch online RL efficacy.

Key Findings

1. Algorithm Class: Contrary to imitation learning (IL) and filtered imitation learning, which historically struggle to benefit substantially from suboptimal demonstrations, value-based RL emerges as a superior approach in batch online RL scenarios. The paper underscores that Q-function-guided learning facilitates the utilization of diverse, autonomously gathered data, thereby overcoming traditional barriers and fostering improvements in policy performance.
2. Policy Extraction Methods: The research identifies implicit policy extraction—conducted by selecting optimal actions within the policy distribution via Q-function guidance—as significantly more effective than explicit extraction. This method, which leverages diverse action distributions without encountering the instability found in explicit extraction, ensures robust policy adaptation during iterative offline training phases.
3. Policy Expressivity: Important contrasts are drawn between less expressive Gaussian policies and more expressive diffusion-based policies. The latter, when combined with implicit extraction techniques, demonstrates superior performance by modeling diverse action distributions more accurately and effectively enabling self-improvement through batch online RL.

Practical Implications and Future Directions

The practical implications of these findings are profound for robotic learning systems, suggesting actionable strategies for designing scalable and self-improving robots. By employing value-based RL with expressive policies and implicit extraction methods, robotics practitioners can significantly enhance autonomous data utilization and policy refinement.

Furthermore, the paper proposes augmenting its recipe with temporally-correlated noise, such as that modeled by the Ornstein-Uhlenbeck process, during autonomous data collection. This addition is shown to enhance sample diversity, leading to improved learning outcomes.

In terms of future research, challenges remain in expanding these insights to environments with discrete action spaces and addressing initial states where policies are non-functional. The exploration of methods to efficiently harness non-successful trajectories presents another area ripe for investigation. Additionally, understanding how the proposed recipe interacts with diverse robotic tasks and refining training hyperparameters could provide further optimization in diverse deployment conditions.

Conclusion

The systematic analysis presented in this paper delivers valuable contributions to the field of robotic reinforcement learning, specifically in scaling autonomous data collection for self-improvement. Through a strategic integration of algorithm choice, policy extraction, and expressivity, batch online RL emerges as a promising paradigm to advance robotic capabilities without extensive human oversight. The foundational principles and empirical insights detailed herein serve to guide both theoretical inquiry and practical application in the pursuit of robust robot learning systems.