I have done creating the virtual environment, creating the Q-table, initializing the q-parameters, then I made a training module and stored it in a numpy
array. After completion of training, I have updated the q-table and now I get the plots for the explorations But how can I code for rate decay? Here is my sample code for every step of the training module,
for step in range(max_steps):
exploration_rate_threshold = random.uniform(0,1)
if exploration_rate_threshold > exploration_rate:
action = np.argmax(q_table[state,:])
else:
action = env.action_space.sample()