MDP: Markov decision making process (i)

MDP: Markov decision-making processes (Markov decision process)

Defined:

A Markov model includes the following sections

• State set S (states)
• Action set A (Actions)
• Rewards and punishments functions R (reward function)
• Under state s , the effect function of a action is performed T

We assume that the effect of performing action a is only relevant to the current state, regardless of the previous history state.

Action Representations: divided into deterministic actions (determinstic actions) and random actions (Stochastic actions)

• Deterministic action:t:sxa-S, for each State and action to determine the next state
• Random action:t:sxa-Prob (S), for each State and action can determine the probability distribution of the next state

Strategy π (Pai): Indicates the current state s select what action a

The execution process of the strategy π:

1. Determining the current state
2. According to the current state, according to the strategy Pi execute action a
3. Perform 1

The so-called full observation (Fully observation) is the next state that performs action A arrives, the system can be known

MDP: Markov decision making process (i)