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Monte Carlo and Temporal Difference Methods in Reinforcement Learning
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DOI：10.1109/MCI.2023.3304145.png)
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Reinforcement learning (RL) is a subset of machine learning that allows intelligent agents to acquire the ability of executing desired actions through interactions with an environment. Its remarkable progress has achieved significant results in diverse domains, such as Go and StarCraft, and practical challenges like protein-folding. This short paper presents overviews of two common RL approaches: the Monte Carlo and temporal difference methods. To obtain a more comprehensive understanding of these concepts and gain practical experience, readers can access the full article on IEEE Xplore, which includes interactive materials and examples.
Keyword:
Machine learning
Monte Carlo methods
Reinforcement learning
Intelligent agents
期刊
IF:
11.2
论文数:
613
被引数:
3.1K
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