def run_task(evaluation_config, network_config, reinforce_config):
env = TowerExample()
max_episode_steps = 10000
state = env.reset()
TOWER_BR, TOWER_BL, TOWER_TR, TOWER_TL = [1, 2, 3, 4]
choose_tower = DQNAdaptive(
name="tower",
choices=[TOWER_BR, TOWER_BL, TOWER_TR, TOWER_TL],
network_config=network_config,
reinforce_config=reinforce_config)
training_summaries_path = evaluation_config.summaries_path + "/train"
clear_summary_path(training_summaries_path)
train_summary_writer = tf.summary.FileWriter(training_summaries_path)
#Training Episodes
for episode in range(evaluation_config.training_episodes):
state = env.reset()
total_reward = 0
episode_summary = tf.Summary()
start_time = time.time()
tower_to_kill, _ = choose_tower.predict(state.state)
end_time = time.time()
action = env.new_action()
env_start_time = time.time()
action.attack_quadrant(tower_to_kill)
state = env.act(action)
counter = 0
choose_tower.reward(state.reward)
total_reward += state.reward
if state.is_terminal():
logger.info("End Episode of episode %d!" % (episode + 1))
logger.info("Total Reward %d!" % (total_reward))
env_end_time = time.time()
logger.debug("Counter: %d" % counter)
logger.debug("Neural Network Time: %.2f" % (end_time - start_time))
logger.debug("Env Time: %.2f" % (env_end_time - env_start_time))
choose_tower.end_episode(state.state)
episode_summary.value.add(tag="Reward", simple_value=total_reward)
train_summary_writer.add_summary(episode_summary, episode + 1)
train_summary_writer.flush()
choose_tower.disable_learning()
test_summaries_path = evaluation_config.summaries_path + "/test"
clear_summary_path(test_summaries_path)
test_summary_writer = tf.summary.FileWriter(test_summaries_path)
choose_tower.explanation = True
explanation = Explanation("Tower Capture", (40, 40))
chart = BarChart("Move Explanation", "Actions", "QVal By Reward Type")
layer_names = ["HP", "Type 1", "Type 2", "Type 3", "Friend", "Enemy"]
#Test Episodes
for episode in range(evaluation_config.test_episodes):
state = env.reset(visualize=evaluation_config.render, record=True)
total_reward = 0
episode_summary = tf.Summary()
tower_to_kill, q_values, saliencies = choose_tower.predict(state.state)
choices = env.actions()['actions']
for choice, action_value in choices.items():
key = choice
explanation.add_layers(layer_names,
saliencies[action_value - 1],
key=key)
group = BarGroup("Attack {}".format(choice), saliency_key=key)
key = choice + "_Overall"
explanation.add_layers(layer_names,
saliencies[action_value - 1],
key=key)
bar = Bar("Attack {}".format(choice),
q_values[action_value - 1],
saliency_key=key)
group.add_bar(bar)
chart.add_bar_group(group)
explanation.with_bar_chart(chart)
action = env.new_action()
action.attack_quadrant(tower_to_kill)
action.skip = False if evaluation_config.render else True
state = env.act(action, explanation=explanation)
while not state.is_terminal():
time.sleep(0.5)
action = env.new_action()
action.skip = False
state = env.act(action, explanation=explanation)
total_reward += state.reward
time.sleep(10)
if state.is_terminal():
logger.info("End Episode of episode %d!" % (episode + 1))
logger.info("Total Reward %d!" % (total_reward))
episode_summary.value.add(tag="Reward", simple_value=total_reward)
test_summary_writer.add_summary(episode_summary, episode + 1)
test_summary_writer.flush()
return out
env = TowerExample()
print("Possible reward types:", env.reward_types())
print("Possible actions:", env.actions())
print("Action description", env.action_desc())
actions = invert_actions(env)
s = env.reset(record=True)
print("acting")
act = env.new_action()
explanation = Explanation("Fake Random Saliency Info", layer_shape=(40, 40))
chart = BarChart("Move Explanation", "Actions", "QVal By Reward Type")
layer_names = ["HP", "Type 1", "Type 2", "Type 3", "Friend", "Enemy"]
max_quad = 0
max_value = -np.inf
for quad in range(1, 5):
layers = np.random.random((40, 40, 6))
key = "BarGroup{}".format(quad)
group = BarGroup("Attack {}".format(actions[quad]), saliency_key=key)
explanation.add_layers(layer_names, layers, key=key)
value = 0.0
for r_type in env.reward_types():
b_layers = np.random.random((40, 40, 6))
key = "BarGroup{}Bar{}".format(quad, r_type)