Reinforcement Learning with TensorFlow

Transition model

The transition model T(s, a, s') is a function of three variables, which are the current state (s), action (a), and the new state (s'), and defines the rules to play the game in the environment. It gives probability P(s'|s, a), that is, the probability of landing up in the new s' state given that the agent takes an action, a, in given state, s.

The transition model plays the crucial role in a stochastic world, unlike the case of a deterministic world where the probability for any landing state other than the determined one will have zero probability.

Let's consider the following environment (world) and consider different cases, determined and stochastic:

Since the actions a  A where, A = {UP, DOWN, RIGHT, and LEFT}.

The ...