mlsanityData to 14 Oct 2026

Adaptive Data Exploitation in Deep Reinforcement Learning

arXiv 2501.12620 ↗First posted 22 Jan 2025cs.LG cs.AIcited by 1

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

We introduce ADEPT: Adaptive Data ExPloiTation, a simple yet powerful framework to enhance the **data efficiency** and **generalization** in deep reinforcement learning (RL). Specifically, ADEPT adaptively manages the use of sampled data across different learning stages via multi-armed bandit (MAB) algorithms, optimizing data utilization while mitigating overfitting. Moreover, ADEPT can significantly reduce the computational overhead and accelerate a wide range of RL algorithms. We test ADEPT on benchmarks including Procgen, MiniGrid, and PyBullet. Extensive simulation demonstrates that ADEPT can achieve superior performance with remarkable computational efficiency, offering a practical solution to data-efficient RL. Our code is available at https://github.com/yuanmingqi/ADEPT.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 36 of its 63 references that are in the map. First posted on arXiv 22 Jan 2025.

Builds on

36 of 63 references.

References · 36

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  4. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
  5. 2017Hindsight Experience Replaycited by 1,136
  6. 2018Exploration by Random Network Distillationcited by 993
  7. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 883
  8. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  9. 2020CURL: Contrastive Unsupervised Representations for Reinforcement Learningcited by 760
  10. 2020Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixelscited by 585
  11. 2020Reinforcement Learning with Augmented Datacited by 468
  12. 2021Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learningcited by 372
24 more
  1. 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Taskscited by 327
  2. 2020Agent57: Outperforming the Atari Human Benchmarkcited by 303
  3. 2018Distributed Prioritized Experience Replaycited by 269
  4. 2020Decoupling Representation Learning from Reinforcement Learningcited by 269
  5. 2020Never Give Up: Learning Directed Exploration Strategiescited by 212
  6. 2018Distributed Distributional Deterministic Policy Gradientscited by 199
  7. 2020The NetHack Learning Environmentcited by 198
  8. 2020Phasic Policy Gradientcited by 123
  9. 2019Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneckcited by 101
  10. 2020Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learningcited by 93
  11. 2021Decoupling Value and Policy for Generalization in Reinforcement Learningcited by 82
  12. 2020Improving Generalization in Reinforcement Learning with Mixture Regularizationcited by 81
  13. 2022EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Enginecited by 62
  14. 2020Smooth Exploration for Robotic Reinforcement Learningcited by 54
  15. 2022Exploration via Elliptical Episodic Bonusescited by 51
  16. 2020Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learningcited by 37
  17. 2023On the Importance of Exploration for Generalization in Reinforcement Learningcited by 31
  18. 2020Rotation, Translation, and Cropping for Zero-Shot Generalizationcited by 24
  19. 2022R'enyi State Entropy for Exploration Acceleration in Reinforcement Learningcited by 18
  20. 2023Improving Generalization in Visual Reinforcement Learning via Conflict-aware Gradient Agreement Augmentationcited by 14
  21. 2022Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalizationcited by 12
  22. 2023Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learningcited by 12
  23. 2022Rethinking Value Function Learning for Generalization in Reinforcement Learningcited by 8
  24. 2022Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learningcited by 7

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learningcited by 1

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  4. 2025Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL
  5. 2024On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond
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  7. 2024MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
  8. 2026Efficient RLVR Training via Weighted Mutual Information Data Selection