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Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control (2405.16158v3)

Published 25 May 2024 in cs.LG

Abstract: Sample efficiency in Reinforcement Learning (RL) has traditionally been driven by algorithmic enhancements. In this work, we demonstrate that scaling can also lead to substantial improvements. We conduct a thorough investigation into the interplay of scaling model capacity and domain-specific RL enhancements. These empirical findings inform the design choices underlying our proposed BRO (Bigger, Regularized, Optimistic) algorithm. The key innovation behind BRO is that strong regularization allows for effective scaling of the critic networks, which, paired with optimistic exploration, leads to superior performance. BRO achieves state-of-the-art results, significantly outperforming the leading model-based and model-free algorithms across 40 complex tasks from the DeepMind Control, MetaWorld, and MyoSuite benchmarks. BRO is the first model-free algorithm to achieve near-optimal policies in the notoriously challenging Dog and Humanoid tasks.

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Citations (4)
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Summary

  • The paper presents the BRO algorithm that scales critic networks using robust regularization and optimism to outperform state-of-the-art continuous control methods.
  • It demonstrates superior performance on 40 tasks, achieving near-optimal results on Dog and Humanoid benchmarks.
  • Extensive experiments with over 15,000 agent trainings highlight the efficacy of scaling for enhancing compute and sample efficiency in RL.

Analysis of the BRO Algorithm: Enhancements in Compute and Sample Efficiency for Continuous Control

The paper "Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control" explores a novel approach to improving sample efficiency in Reinforcement Learning (RL), specifically for continuous control tasks, through scaling methods that go beyond traditional algorithmic improvements. The core proposal of the paper is the BRO (Bigger, Regularized, Optimistic) algorithm, which blends model capacity scaling with robust regularization techniques and optimistic exploration strategies to enhance the RL landscape.

Summary and Contributions

The paper demonstrates that the scalability of model architectures, chiefly the critic network, along with effective regularization and optimistic exploration techniques, can achieve exceptional performance in continuous control settings. The authors conduct extensive empirical evaluations to validate this approach, leading to several key contributions:

  1. BRO Algorithm: Introducing the BRO framework, the paper showcases how a scaled critic model with strong regularization and optimism significantly outperforms existing model-based and model-free algorithms. Specifically, BRO achieves outstanding results on 40 complex tasks across various benchmarks, including the DeepMind Control, MetaWorld, and MyoSuite environments. Noteworthy accomplishments include nearing optimal policies on challenging Dog and Humanoid tasks.
  2. Empirical Insights: The research offers profound insights into the interplay between model scaling and algorithmic enhancements. The analysis of critic network scaling in continuous deep RL, backed by rigorous experiments involving over 15,000 agent trainings, identifies design elements essential for more efficient scaling.
  3. Regularization and Optimism: The BRO approach integrates Layer Norm and weight decay for regularization and employs optimistic exploration strategies to balance exploration-exploitation effectively. Notably, the paper finds that strong regularization can obviate the need for pessimistic Q-value adjustments.
  4. Architecture and Scaling Analysis: The comparison of various network architectures highlights that BRO's BroNet architecture offers superior scaling capabilities, achieving near-optimal results without significant performance degradation, even with increased model size. The authors also stress the importance of regularization in achieving stable and robust scaling, especially in more complex tasks.
  5. Sample and Compute Efficiency: The research elucidates how parameter scaling primarily on the critic side yields computational benefits in parallelized settings, enhancing sample efficiency while reducing the compute load compared to solely increasing the replay ratio.

Practical and Theoretical Implications

The findings have important implications for the future of RL research and practice. The development of the BRO algorithm underscores the potential of scaling neural architectures in RL, highlighting the significance of leveraging model scaling combined with strategic algorithmic adjustments. Practically, these insights can propel advancements in autonomous systems where continuous control is critical, offering more efficient solutions without excessively demanding computational resources.

Theoretically, the research challenges conventional practices in continuous RL settings, particularly the focus on relatively small network architectures. By showcasing the success of scaling strategies, this work encourages RL researchers to explore further innovations that harness larger, regularized models to tackle increasingly complex control tasks.

Future Directions

This paper opens new avenues for RL research, particularly in continuous control domains. Future work could explore the adaption of the BRO framework or its principles to discrete action settings or tasks requiring real-time decision-making capabilities. Moreover, the implications of SADL-based optimization frameworks or alternative architecture regularization strategies present rich grounds for further investigation, potentially leading to even greater performance and efficiency gains.

Overall, the BRO algorithm exemplifies the advancement of RL through meticulous model scaling and regularization, setting a new benchmark in sample efficiency and compute effectiveness for continuous action scenarios.

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