Difference between revisions of "Markov Decision Process (MDP)"
Line 2: | Line 2: | ||
* [[Deep Reinforcement Learning (DRL)]] | * [[Deep Reinforcement Learning (DRL)]] | ||
+ | * [[Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)]] | ||
https://upload.wikimedia.org/wikipedia/commons/thumb/a/ad/Markov_Decision_Process.svg/600px-Markov_Decision_Process.svg.png | https://upload.wikimedia.org/wikipedia/commons/thumb/a/ad/Markov_Decision_Process.svg/600px-Markov_Decision_Process.svg.png | ||
Line 14: | Line 15: | ||
<youtube>i0o-ui1N35U</youtube> | <youtube>i0o-ui1N35U</youtube> | ||
<youtube>Csiiv6WGzKM</youtube> | <youtube>Csiiv6WGzKM</youtube> | ||
− | <youtube> | + | <youtube>tO6hTI8CXaM</youtube> |
Revision as of 06:09, 27 May 2018
Used where outcomes are partly random and partly under the control of a decision maker. MDP is a discrete time stochastic control process. At each time step, the process is in some state s, and the decision maker may choose any action a that is available in state s. The process responds at the next time step by randomly moving into a new state s', and giving the decision maker a corresponding reward R_{a}(s,s')} R_a(s,s'). The probability that the process moves into its new state s' is influenced by the chosen action.