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Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning (2107.09645v1)

Published 20 Jul 2021 in cs.AI and cs.LG

Abstract: We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmentation to learn directly from pixels. We introduce several improvements that yield state-of-the-art results on the DeepMind Control Suite. Notably, DrQ-v2 is able to solve complex humanoid locomotion tasks directly from pixel observations, previously unattained by model-free RL. DrQ-v2 is conceptually simple, easy to implement, and provides significantly better computational footprint compared to prior work, with the majority of tasks taking just 8 hours to train on a single GPU. Finally, we publicly release DrQ-v2's implementation to provide RL practitioners with a strong and computationally efficient baseline.

Citations (292)

Summary

  • The paper introduces DrQ-v2, a model-free RL algorithm that leverages data augmentation to achieve superior sample and computational efficiency for visual continuous control tasks.
  • It replaces SAC with DDPG, employs multi-step returns and adaptive exploration noise, addressing exploration inefficiencies and stabilizing reward propagation.
  • DrQ-v2 demonstrates state-of-the-art performance on DMC tasks, achieving training speeds nearly 4x faster and solving complex humanoid locomotion solely from pixels.

Mastering Visual Continuous Control through DrQ-v2: A Performance and Efficiency-Driven Approach

The paper at hand presents DrQ-v2, an evolved model-free reinforcement learning (RL) algorithm specifically designed to tackle visual continuous control tasks. Building upon the foundations laid by DrQ, DrQ-v2 leverages data augmentation techniques to effectively learn from high-dimensional pixel inputs. This updated version not only enhances learning capabilities in terms of sample efficiency but also significantly optimizes computational resources, facilitating faster training times. The algorithm demonstrates state-of-the-art performance in the DeepMind Control (DMC) Suite, successfully addressing complex humanoid locomotion tasks solely from pixel observations, an endeavor previously unaccomplished by model-free RL methods.

Core Enhancements and Methodology

DrQ-v2 introduces a comprehensive suite of improvements over its predecessor, DrQ. Key modifications include transitioning the underlying RL algorithm from Soft Actor-Critic (SAC) to Deep Deterministic Policy Gradient (DDPG), allowing for the integration of multi-step returns, which enhances reward propagation. This transition addresses exploration inefficiencies noted in SAC, particularly mitigating premature entropy collapse. The algorithm applies random shift data augmentation, incorporating additional interpolation to stabilize training and boost performance.

Another significant enhancement is the adaptive schedule for exploration noise, which modulates the standard deviation over time, yielding better exploration during initial training phases and convergent performance in later stages. Furthermore, DrQ-v2 makes strides in efficiency, evidenced by a meticulous hyperparameter tuning process that optimizes replay buffer size, mini-batch size, and learning rates.

Implementation and Computational Optimization

DrQ-v2's implementation addresses critical computational bottlenecks encountered in prior versions. Enhancements in replay buffer management and data augmentation processing have been realized, resulting in an increase in FPS throughput from 28 to 96 on equivalent hardware configurations. These optimizations mean that tasks can now be solved significantly faster in terms of wall-clock time—most tasks within just eight hours—a considerable improvement over earlier frameworks and methods, thus reducing hardware intensity and making it accessible for broader research endeavors.

Experimental Evaluation and Comparative Performance

Empirical evaluations present DrQ-v2 as a robust algorithm excelling across a variety of task difficulties within the DMC Suite. It outperforms other model-free baselines like CURL and DrQ, markedly on challenging tasks requiring intricate control policies, such as quadruped and humanoid locomotion. Additionally, when pitted against Dreamer-v2, a leading model-based method, DrQ-v2 mirrors sample efficiency while achieving execution speeds approximately four times faster regarding wall-clock time.

The experimentation setup, utilizing a single GPU configuration, highlights DrQ-v2’s prowess not only in convergency rates but also in its ability to democratize visual RL research, offering a scalable solution to resource-constrained research environments. By effectively bridging the gap between sample efficiency and computational efficiency, this algorithm lays important groundwork for future research in enhancing model-free approaches to visual continuous control.

Theoretical and Practical Implications

DrQ-v2’s advancements have profound implications on the theoretical landscape of RL and its practical deployment in complex environments. The findings affirm the potential viability of model-free methods in tackling visual tasks that were dominantly the territory of model-based approaches, prompting a re-evaluation of the trade-offs between these methodologies. On a practical forefront, DrQ-v2's open-source release provides an accessible and efficient baseline for practitioners aiming to apply RL in high-dimensional visual domains.

Future Trajectories

The research opens avenues for further exploration in model-free RL implementation efficiencies and the potential advantages of integrating richer augmentation techniques or advanced policy exploration strategies. Moreover, the successful resolution of humanoid locomotion tasks from pixels heralds possibilities for upscaling DrQ-v2 to more intricate and diverse application domains.

In conclusion, DrQ-v2 stands as an accomplished model-free RL solution that significantly ameliorates visual continuous control's dual challenges of performance and efficiency. Its deployment not only advances the understanding of visual RL problem-solving but also equips the research community with a refined toolset for addressing increasingly complex real-world tasks.