Papers › Adaptive Data Exploitation in Deep Reinforcement Learning

Adaptive Data Exploitation in Deep Reinforcement Learning

22 Jan 2025arXiv:2501.12620archive 2025-07-28

Mingqi Yuan, Bo Li, Xin Jin, Wenjun Zeng

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.

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Computational Efficiency Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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