markov-models | 易学教程

How do Markov Chains work and what is memorylessness?

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问题 How do Markov Chains work? I have read wikipedia for Markov Chain, But the thing I don't get is memorylessness. Memorylessness states that: The next state depends only on the current state and not on the sequence of events that preceded it. If Markov Chain has this kind of property, then what is the use of chain in markov model? Explain this property. 回答1: You can visualize Markov chains like a frog hopping from lily pad to lily pad on a pond. The frog does not remember which lily pad(s) it

Markov Model descision process in Java

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I'm writing an assisted learning algorithm in Java. I've run into a mathematical problem that I can probably solve, but because the processing will be heavy I need an optimum solution. That being said, if anyone knows a optimized library that will be totally awesome, but the language is Java so that will need to be taken into consideration. The idea is fairly simple: Objects will store combination of variables such as ABDC, ACDE, DE, AE. The max number of combination will be based on how many I can run without slowing down the program, so theoretically 100 lets say. The decision process will

What is the difference between value iteration and policy iteration?

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问题 In reinforcement learning, what is the difference between policy iteration and value iteration ? As much as I understand, in value iteration, you use the Bellman equation to solve for the optimal policy, whereas, in policy iteration, you randomly select a policy π, and find the reward of that policy. My doubt is that if you are selecting a random policy π in PI, how is it guaranteed to be the optimal policy, even if we are choosing several random policies. 回答1: Let's look at them side by side

What is the difference between value iteration and policy iteration?

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In reinforcement learning, what is the difference between policy iteration and value iteration ? As much as I understand, in value iteration, you use the Bellman equation to solve for the optimal policy, whereas, in policy iteration, you randomly select a policy π, and find the reward of that policy. My doubt is that if you are selecting a random policy π in PI, how is it guaranteed to be the optimal policy, even if we are choosing several random policies. zyxue Let's look at them side by side. The key parts for comparison are highlighted. Figures are from Sutton and Barto's book: