Created
October 16, 2022 06:34
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SARSA training function
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def train(n_episodes, n_max_steps, start_epsilon, min_epsilon, decay_rate, Qtable): | |
for episode in range(n_episodes): | |
# Reset the environment at the start of each episode | |
state, info = env.reset() | |
t = 0 | |
done = False | |
# Calculate epsilon value based on decay rate | |
epsilon = max(min_epsilon, (start_epsilon - min_epsilon)*np.exp(-decay_rate*episode)) | |
# Choose an action using previously defined epsilon-greedy policy | |
action = epsilon_greedy(Qtable, state, epsilon) | |
for t in range(n_max_steps): | |
# Perform the action in the environment, get reward and next state | |
next_state, reward, done, _, info = env.step(action) | |
# Choose next action | |
next_action=epsilon_greedy(Qtable, next_state, epsilon) | |
# Update Q-table | |
Qtable = update_Q(Qtable, state, action, reward, next_state, next_action) | |
# Update current state | |
state = next_state | |
action = next_action | |
# Finish the episode when done=True, i.e., reached the goal or fallen into a hole | |
if done: | |
break | |
# Return final Q-table | |
return Qtable |
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