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Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

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arxiv 2107.09645 v1 pith:FQLV5H64 submitted 2021-07-20 cs.AI cs.LG

Denis Yarats , Rob Fergus , Alessandro Lazaric , Lerrel Pinto This is my paper
classification cs.AIcs.LG
keywords drq-v2controlcontinuousdirectlylearningmodel-freereinforcementtasks
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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.

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Cited by 15 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments

    cs.LG 2025-05 conditional novelty 7.0 of 10

    Across noisy DeepMind Control tasks, explicit bisimulation-metric losses add little denoising benefit beyond plain self-prediction and feature normalization, which dominate performance.

  2. OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A policy-agnostic two-stage real-world RL method learns tactile residual corrections on frozen visual policies, lifting contact-rich task success from 5–40% to 85–100% in under 80 minutes.

  3. Error Amplification Limits ANN-to-SNN Conversion in Continuous Control

    cs.NE 2026-01 conditional novelty 6.0 of 10

    Temporally correlated action errors, amplified by closed-loop dynamics, explain ANN-to-SNN conversion failures in continuous control, and cross-step residual potential initialization mitigates them.

  4. Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach

    cs.AI 2025-10 conditional novelty 6.0 of 10

    A sample-efficient SAC-based method with replay-ratio resets, offline data bootstrapping, and expert-driven fine-tuning produces a goalkeeper that outperforms the built-in AI in EA SPORTS FC 25.

  5. DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation

    cs.RO 2025-09 conditional novelty 6.0 of 10

    Depth-guided masking improves visual RL generalization, sample efficiency, and interpretability on manipulation tasks.

  6. Touch begins where vision ends: Generalizable policies for contact-rich manipulation

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A localize-then-execute policy that combines vision-language reaching, semantic background augmentation, and residual reinforcement learning with tactile sensing reaches about 90% success on millimeter-precision manip...

  7. The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning

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    Introduces LEAST, an adaptive early-episode-stopping rule for off-policy deep RL that improves learning efficiency on MuJoCo and DeepMind Control benchmarks.

  8. Differential Amplifier-Inspired AmpAttention for Multi-View Robotic Manipulation

    cs.RO 2026-07 conditional novelty 5.0 of 10

    AmpAttention and RVAF raise multi-view robotic manipulation success and cut training time by suppressing attention noise with a differential-amplifier-style mechanism plus a CMRR loss.

  9. What Matters for Simulation to Online Reinforcement Learning on Real Robots

    cs.RO 2026-02 conditional novelty 5.0 of 10

    Sim-to-online RL on three real robots is stabilized by retaining data, warm-starting the replay buffer, and using asymmetric actor-critic updates with a low actor learning rate.

  10. ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning

    cs.RO 2025-12 conditional novelty 5.0 of 10

    ReinforceGen uses imitation learning, RL fine-tuning of skill policies, and real-time pose replanning to reach over 80% success on five long-horizon Robosuite tasks from only 10 human demonstrations.

  11. RoDyn: Taming Interactive Robot-Dynamic 2.5D World Model for Robotic Manipulation

    cs.RO 2025-10 unverdicted novelty 5.0 of 10

    Abstract describes RoDyn but full text describes iMoWM; the record is internally inconsistent and the headline claims are absent from the body.

  12. AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation

    cs.RO 2025-07 conditional novelty 5.0 of 10

    AC-DiT adds mobility-to-body conditioning and perception-aware 2D/3D weighting to a diffusion transformer, improving success rates on simulated and real-world mobile manipulation tasks.

  13. Residual Reward Models for Preference-based Reinforcement Learning

    cs.LG 2025-07 conditional novelty 5.0 of 10

    Combining a hand-designed or learned prior reward with a preference-trained residual improves sample efficiency and final performance in preference-based reinforcement learning.

  14. Fast-in-Slow: A Dual-System Foundation Model Unifying Fast Manipulation within Slow Reasoning

    cs.RO 2025-06 conditional novelty 5.0 of 10

    FiS-VLA embeds a diffusion-based action module into the final transformer blocks of a vision-language model, achieving 69% mean success on RLBench and a claimed 117.7 Hz control frequency.

  15. A Survey of State Representation Learning for Deep Reinforcement Learning

    cs.LG 2025-06 conditional novelty 4.0 of 10

    A six-class taxonomy of state representation learning methods for model-free online deep reinforcement learning, with selection guidelines, evaluation metrics, and future directions.

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