Markov decision process


## Find most effective Policy from random
Reinforcement Learning is process of resolving MDP


> New twist - don’t know R and T (different to traditional MDP) → **(Step 1)**
>> → Must actually try actions and states out to learn

> - A set of states s ∈S 
> - A set of actions (per state) A 
> - A **model T(s,a,s’) **(probability)
> - A **reward function** **R(s,a,s’)**


> Bellman equation - do not need to know environment model use action -value function



### MDP
> [OpenRL - 강화학습 그리고 OpenAI - 2: Intro to Reinforcement Learning (1) MDP &amp;Value Function](http://www.modulabs.co.kr/RL_library/2136)

> MDP  is Markov Decision Process and tuple of states, actions, station probability matrix (probability to go certain state to another state by certain action), reward function, discounted factor

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