Reinforcement Learning_By David Silver笔记二: Markov Decision Processes
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- Markov Process
Markov Reward Process
直接求解的时间复杂度是O(N^3), 对于small MRPs，可使用直接计算的方法，对于large MRPs使用如下迭代法：动态规划，蒙特卡洛评估，时序差分学习Markov Decision Process (Markov reward process with decisions)
- a policy is a distribution over actions given states. GIven an MDP and policy, the state sequence is Markov process, the state and reward sequence is Markov reward process.
- state-value function of an MDP is the expected return starting from state and then following policy
- action-value function is the expected return starting from state, taking action and following policy
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- Reinforcement Learning_By David Silver笔记二: Markov Decision Processes
- Reinforcement Learning_By David Silver笔记一: Introduction
- Reinforcement Learning_By David Silver笔记三: Planning by Dynamic Programming
- Reinforcement Learning_By David Silver笔记四: Model Free Prediction
- Reinforcement Learning_By David Silver笔记五: Model Free Control
- Reinforcement Learning_By David Silver笔记六: Value Function Approximation
- Reinforcement Learning_By David Silver笔记七: Policy Gradient Methods
- Reinforcement Learning_By David Silver笔记八: Integrating Learning and Planning
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