Asynchronous Deep Reinforcement Learning
2025/02/19
再生時間： 18 分
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Asynchronous Deep Reinforcement Learning

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Mnih et al.'s paper introduces asynchronous methods for deep reinforcement learning, enhancing the training of deep neural network controllers. The core idea involves parallel actor-learners, each exploring different environment instances, which stabilises training. Their asynchronous advantage actor-critic (A3C) method achieves state-of-the-art results on Atari games, surpassing existing GPU-based algorithms with less computational demand, and demonstrating success in continuous control tasks and 3D maze navigation. The supplementary material provides greater detail on the optimization techniques used, the experimental setups, and shows performance on both discrete and continuous control tasks. The experiments highlight the scalability, data efficiency, and robustness of the proposed asynchronous algorithms compared to existing approaches.
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あらすじ・解説

Mnih et al.'s paper introduces asynchronous methods for deep reinforcement learning, enhancing the training of deep neural network controllers. The core idea involves parallel actor-learners, each exploring different environment instances, which stabilises training. Their asynchronous advantage actor-critic (A3C) method achieves state-of-the-art results on Atari games, surpassing existing GPU-based algorithms with less computational demand, and demonstrating success in continuous control tasks and 3D maze navigation. The supplementary material provides greater detail on the optimization techniques used, the experimental setups, and shows performance on both discrete and continuous control tasks. The experiments highlight the scalability, data efficiency, and robustness of the proposed asynchronous algorithms compared to existing approaches.

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