Markov Decisions Process
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A Markov decision process (MDP), is a discrete-time Stochastic Processes, and are an extension of Markov chains for decision making theory.
An MDP is made up of
- A set of states S
- A set of actions A
- A transition kernel P_a(s, s\prime) = Pr[s_{t+1} = s\prime | s_t = s, a_t = a]
- And the immediate reward function R_a(s, s\prime)
It is used in Reinforcement Learning as the core data generation process.
Markov Chain
See Probability course for more info.
Properties
Aperiodic
The period of a state is the largest integer d such that P(s_i, s_i)^n = 0 for every n not divisible by d. The period of i is shown as d(s_i). If P(s_i, s_i)^n = 0 for all d then d(s_i) = \infty.
for a state s_i
- Periodic: if d(s_i) > 1
- Aperiodic: if d(s_i) = 1.
A Markov Chain is said to be aperiodic if all its states are aperiodic.
Irreducible
An irreducible Markov Chain or Markov Decisions Process is one in which the
Positive Recurrent
Ergodic
An MDP is said to be ergodic if all its states are ergodic. A state s_i is said to be ergodic if it is aperiodic and positive recurrent. In other words