Adaptive Data Exploitation in Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2501.12620v1
• Date: Wed, 22 Jan 2025 04:01:17 GMT
• Title: Adaptive Data Exploitation in Deep Reinforcement Learning
• Authors: Mingqi Yuan, Bo Li, Xin Jin, Wenjun Zeng,
• Abstract summary: We introduce ADEPT, a powerful framework to enhance the **data efficiency** and **generalization** in deep reinforcement learning (RL)<n>Specifically, ADEPT adaptively manages the use of sampled data across different learning stages via multi-armed bandit (MAB) algorithms.<n>We test ADEPT on benchmarks including Procgen, MiniGrid, and PyBullet.
• Score: 50.53705050673944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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