https://arxiv.org/abs/2306.09200
Historical Replay for Reinforcement Learning By Lilian Weng Word count: 575 words Average read time: 3 minutes Source code: Link
Summary: Historical replay is a technique used in reinforcement learning where the agent replays and re-learns from past experiences. This is in contrast to online learning where the agent learns sequentially from each new experience. Historical replay provides two main benefits:
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Breaks correlation between consecutive experiences. By sampling experiences from the past at random, the agent avoids overfitting to recent experiences. This leads to more robust learning.
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Allows for off-policy learning. The agent can learn from experiences generated by other behavior policies, not just the current target policy. This exposes the agent to a more diverse set of experiences, enabling better exploration.
There are two common ways to implement historical replay:
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Experience replay - Store experiences in a buffer and sample uniformly from the buffer. This breaks correlation and enables off-policy learning.
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Prioritized experience replay - Weight sampling so that important, rare experiences have a higher chance of being selected. This can accelerate learning.
Historical replay is a core technique used in modern deep reinforcement learning to achieve good performance, especially in complex environments. When combined with a target network, it enables stable deep Q-learning.
This content provides a good overview of how historical replay works in the context of reinforcement learning. The main concepts around breaking correlation, enabling off-policy learning, experience replay buffers, and prioritized experience replay are clearly explained.
The content would be highly applicable to developing reinforcement learning systems using neural networks as function approximators. Experience replay is crucial for training deep Q-networks and actor-critic algorithms. The concepts would extend to future applications of large language models and GANs for reinforcement learning as well. Overall this is a helpful resource for understanding a foundational RL technique.
