Keras Reinforcement Learning Projects

SARSA

The State-action-reward-state-action (SARSA) algorithm implements an on-policy time differences method, in which the update of the action-value function is performed based on the outcome of the transition from state s to state s' through action a, based on a selected policy π (s, a).

There are policies that always choose the action that provides the maximum reward and non-deterministic policies (ε-greedy, ε-soft, softmax), which ensure an element of exploration in the learning phase.

Greedy is a term used to represent a family of algorithms trying to get a global solution, through excellent local choices.

In SARSA, it is necessary to estimate the action-value function q (s, a), because the total value of a state v (s) (value function) ...