(action, )] + params.LEARNING_RATE * (
reward + params.DISCOUNT * expected_q)
elif new_state[0] >= params.env.goal_position:
print(f"finish in episode {episode}")
print(
f'Episode: {episode:>5d}, average reward: {average_reward:>4.1f}, current epsilon: {params.EPSILON:>1.2f}, discount: {params.DISCOUNT:>2.2f},Learning Rate: {params.LEARNING_RATE}'
)
table[discrete_state + (action, )] = params.REWARD_END
discrete_state = new_discrete_state
episode_reward += reward
# Get the BEST Value in all episodes
if params.get_bestValue() < episode_reward:
params.set_bestValue(episode_reward)
params.set_bestEpisode(episode)
params.set_bestLEARNING_RATE(step_size)
#params.set_bestDISCOUNT(discount)
best_stepsize[(agent, step_size)].append(episode_reward)
if agent == qLearningAgent:
QLearning_stats['episode'].append(episode)
QLearning_stats['learning_rate'].append(step_size)
QLearning_stats['best_value'].append(episode_reward)
#QLearning_best['episode'] = episode
#QLearning_best['learning_rate'] = episode
#QLearning_best['best_value'] = episode
elif agent == sarsaAgent:
SARSA_stats['episode'].append(episode)
(action, )] + params.LEARNING_RATE * (
reward + params.DISCOUNT * expected_q)
elif new_state[0] >= params.env.goal_position:
print(f"finish in episode {episode}")
print(
f'Episode: {episode:>5d}, average reward: {average_reward:>4.1f}, current epsilon: {params.EPSILON:>1.2f}, discount: {params.DISCOUNT:>2.2f},Learning Rate: {params.LEARNING_RATE}'
)
table[discrete_state + (action, )] = params.REWARD_END
discrete_state = new_discrete_state
episode_reward += reward
# Get the BEST Value in all episodes
if params.get_bestValue() < episode_reward:
params.set_bestValue(episode_reward)
params.set_bestEpisode(episode)
# EPSILON DECAY for exploratory
if params.END_EPSILON_DECAYING >= episode >= params.START_EPSILON_DECAYING:
params.set_epsilon(params.epsilon_decay_value)
# show some STATS
ep_rewards.append(episode_reward)
# Append the sum of reward at the end of the episode
totalReward[type(agent).__name__].append(episode_reward)
if not episode % params.STATS_EVERY:
