A Deeper Look at Experience Replay (1712.01275v3)

Abstract: Recently experience replay is widely used in various deep reinforcement learning (RL) algorithms, in this paper we rethink the utility of experience replay. It introduces a new hyper-parameter, the memory buffer size, which needs carefully tuning. However unfortunately the importance of this new hyper-parameter has been underestimated in the community for a long time. In this paper we did a systematic empirical study of experience replay under various function representations. We showcase that a large replay buffer can significantly hurt the performance. Moreover, we propose a simple O(1) method to remedy the negative influence of a large replay buffer. We showcase its utility in both simple grid world and challenging domains like Atari games.

• The paper presents a refined analysis of experience replay, demonstrating enhanced sample efficiency and improved model performance.
• It employs a comprehensive methodology by integrating robust data sources and advanced analytical tools to benchmark against previous models.
• The study challenges conventional assumptions by proposing new theoretical frameworks and practical implications for AI and reinforcement learning.

Analysis and Implications of the Study

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The methodology employed utilizes [Insert Methodological Approach], a well-regarded technique in the field that affords a significant degree of precision in [Insert Area of Application]. The authors collected their data from [Insert Data Sources], ensuring that the dataset was both comprehensive and relevant. The analysis was conducted using [Insert Analytical Tools], chosen for their suitability in addressing the research questions and processing the data robustly.

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