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This course will provide a comprehensive introduction to reinforcement learning as an approach to artificial intelligence, emphasizing the design of complete agents interacting with stochastic, incompletely known environments. Reinforcement learning has adapted key ideas from machine learning, operations research, psychology, and neuroscience to produce some strikingly successful engineering applications. The focus is on algorithms for learning what actions to take, and when to take them, so as to optimize long-term performance. This may involve sacrificing immediate reward to obtain greater reward in the long-term or just to obtain more information about the environment. The course will cover Markov decision processes, dynamic programming, temporal-difference learning, Monte Carlo reinforcement learning methods, eligibility traces, the role of function approximation, and the integration of learning and planning. The course will emphasize the development of intuition relating the mathematical theory of reinforcement learning to the design of human-level artificial intelligence.
通过条件成 绩：60 分
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Chapter1
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Chapter2
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Chapter3
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Chapter4
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Chapter5
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Chapter6
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Chapter7
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Chapter8
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Chapter9
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Chapter10
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Chapter11
- 课程介绍
- 课程安排
- 評論
