def __init__(self):
config_obj = get_config()
create_save_dir()
state_size = config_obj["state_size"]
self.model = build_ac_model()
model_input = tf.convert_to_tensor(np.random.random((1, state_size)),
dtype=tf.float32)
self.model(model_input)
self.total_steps = 0
self.game_adapter = GameAdapter.create(get_config()['name'])
def build_ac_model():
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Input
action_size = get_config()['action_size']
state_size = get_config()['state_size']
input_layer = Input(shape=(state_size))
actor_dense = Dense(128, activation='relu')(input_layer)
actor_logits = Dense(action_size, name='actor_logits')(actor_dense)
critic_dense = Dense(128, activation='relu')(input_layer)
critic_values = Dense(1, name='critic_values')(critic_dense)
return Model(inputs=[input_layer], outputs=[actor_logits, critic_values])
def __init__(self):
config_obj = get_config()
create_save_dir()
state_size = config_obj["state_size"]
ac_model = build_ac_model()
model_input = tf.convert_to_tensor(
np.random.random((1, state_size)),
dtype=tf.float32
)
ac_model(global_model, model_input)
self.global_model = global_model
self.global_episode = 0
self.global_moving_average_reward = 0
self.best_score = 0
def train_worker(lock, result_queue, worker_idx):
total_step = 1
mem = Memory()
name = "worker-{}-{}".format(get_config()['name'], worker_idx)
local_model = get_ac_model()
game_adapter = GameAdapter.create(name)
done = False
while not done:
print('hi')
current_state = game_adapter.reset()
mem.clear()
(ep_steps) = train_ep(local_model, game_adapter, current_state)
total_step += ep_steps
lock.acquire()
result_queue.put(None)
lock.release()
if total_step > WORKER_CHECK_STEPS_INTERVAL:
lock.acquire()
done = worker_done(master_agent)
lock.release()
def worker_done(master_agent):
return master_agent.global_episode > get_config()['max_eps']
def is_done(self):
return self.total_steps > get_config()["max_eps"]
def train_random():
config_obj = get_config()
random_agent = build_random_model(config_obj['max_eps'])
run_random_model(random_agent)
def get_save_path(file_name):
config_obj = get_config()
return os.path.join(get_save_dir(),
file_name.format('{}-' + config_obj['name']))
def get_save_dir():
config_obj = get_config()
return 'chase_trainer/data/' + config_obj['name']