while settings.episodes > episode:
# Prepare environment for playing
env.reset()
# Reset or increment values
terminal = False
episode += 1
step = 0
q_max_arr = []
reward_arr = []
epsilon_arr = []
while not terminal and step < settings.train_step_limit:
step += 1
# Get the Q-values of the current state
state_row = env.actor_state_row()
q_values = q_table.get_state_q(state_row)
# Save max(Q(s,a)) for stats
q_max = np.max(q_values)
# Anneal epsilon
if epsilon > settings.final_epsilon:
epsilon = settings.initial_epsilon - (2 * episode /
float(settings.episodes))
else:
# Final epsilon reached, stop annealing.
epsilon = settings.final_epsilon
# Select action
if (np.random.random() < epsilon):
# Choose random action
