def test_paddle_positions():
ball_pos = ((67,17),(72,22))
prev_ball_pos = ((68,18),(73,23))
weights = file_utils.load_weights()
mbb = feature_extractors.MockBoundingBoxExtractor(ball_pos,prev_ball_pos)
domain = []
range_weights = []
actions = [3,4]
for x in range(0, 100):
state = {}
best_score = None
features = mbb.get_features_paddle_x(state, actions, x)
for feature_set in features:
score = 0
for f, v in feature_set:
score += weights[f] * v
if best_score == None or score > best_score:
best_score = score
if best_score != None:
domain.append(x)
range_weights.append(best_score)
plt.scatter(domain, range_weights)
plt.show()
print '############################\n'
if episode != 0 and episode % RECORD_WEIGHTS_PERIOD == 0 and record_weights:
file_utils.save_rewards(rewards, filename='episode-{}-{}-rewards'.format(episode, type(agent).__name__))
file_utils.save_weights(agent.weights, filename='episode-{}-{}-weights'.format(episode, type(agent).__name__))
if agent.explorationProb > MINIMUM_EXPLORATION_EPSILON:
agent.explorationProb -= reduce_exploration_prob_amount
print('episode: {} ended with score: {}'.format(episode, total_reward))
ale.reset_game()
return rewards
if __name__ == '__main__':
game = 'asterix.bin'
gamepath = os.path.join('roms', game)
agent = LEARNING_ALGORITHM
ale = ALEInterface()
ale.loadROM(gamepath)
actions = ale.getMinimalActionSet()
agent.actions = actions;
print actions
if LOAD_WEIGHTS:
agent.weights = file_utils.load_weights(WEIGHTS_FILENAME)
rewards = train_agent(gamepath, agent,
n_episodes=NUM_EPISODES,
display_screen=DISPLAY_SCREEN,
record_weights=RECORD_WEIGHTS,
reduce_exploration_prob_amount=EXPLORATION_REDUCTION_AMOUNT,
n_frames_to_skip=NUM_FRAMES_TO_SKIP)
episode,
type(agent).__name__))
if agent.explorationProb > MINIMUM_EXPLORATION_EPSILON:
agent.explorationProb -= reduce_exploration_prob_amount
print('episode: {} ended with score: {}'.format(episode, total_reward))
ale.reset_game()
return rewards
if __name__ == '__main__':
game = 'asterix.bin'
gamepath = os.path.join('roms', game)
agent = LEARNING_ALGORITHM
ale = ALEInterface()
ale.loadROM(gamepath)
actions = ale.getMinimalActionSet()
agent.actions = actions
print actions
if LOAD_WEIGHTS:
agent.weights = file_utils.load_weights(WEIGHTS_FILENAME)
rewards = train_agent(
gamepath,
agent,
n_episodes=NUM_EPISODES,
display_screen=DISPLAY_SCREEN,
record_weights=RECORD_WEIGHTS,
reduce_exploration_prob_amount=EXPLORATION_REDUCTION_AMOUNT,
n_frames_to_skip=NUM_FRAMES_TO_SKIP)
