Policy Iteration  Python Automation and Machine Learning for ICs   An Online Book  

Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/  


Chapter/Index: Introduction  A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  Appendix  
================================================================================= Policy iteration is another dynamic programming algorithm used in reinforcement learning to find the optimal policy in a Markov decision process (MDP). Like value iteration, policy iteration is an iterative algorithm that alternates between two steps: policy evaluation and policy improvement. The algorithm continues these steps until convergence to find the optimal policy. The stepbystep policy iteration is:
 [3679a] This equation expresses the expected cumulative reward for being in state and following policy . [3679b] The new policy is greedy with respect to the current value function.In some cases, hybrid approaches that combine elements of both value iteration and policy iteration (see page3678) can be effective. For example, one might use a few iterations of value iteration to quickly obtain a reasonable approximation and then switch to policy iteration for finetunin ============================================


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