  Q-learning Mathematics education

Python Methods and Functions

The following derivations will use characters defined as in the required clause.
Q-learning methodology is based on the Bellman equation . where,
E: pending
t + 1 : next state : discount factor

To rephrase the above equation as a Q-value: —  Optimal Q value is defined as Policy iteration: is the process of determining the optimal policy for the model, which consists of the following two steps:

1. Policy Evaluation: This process evaluates the value long-term remuneration function waiting with greedy policy from the last step of policy enhancement.
2. Policy enhancement: this process updates the policy with an action that maximizes V for each state. This process is repeated until convergence is achieved.

Steps included are —

• Initialization: = any valid random number = any A (s) randomly selected

• Policy Evaluation:
while ( )  { for each s in S  {     }}
• Policy improvements:
while (true)   for each s in S  {     if ( )     if ( )   break from both loo ps }  return V,
• Value iteration: this process updates the function V according to the optimal Bellman equation . Working steps:

• Initialization: initialize array V with any random real number.
• Optimal value calculation:
while ( )  { for each s in S  {     }}    return