Numerical Computing with Python

SARSA on-policy TD control

State-action-reward-state-action (SARSA) is an on-policy TD control problem, in which policy will be optimized using policy iteration (GPI), only time TD methods used for evaluation of predicted policy. In the first step, the algorithm learns a SARSA function. In particular, for an on-policy method we estimate q_π (s, a) for the current behavior policy π and for all states (s) and actions (a), using the TD method for learning v_π.

Now, we consider transitions from state-action pair to state-action pair, and learn the values of state-action pairs:

This update is done after every transition from a non-terminal state ...