from environments import Cifar10VGG16
from agents import Agent
import numpy as np
if __name__ == '__main__':
for i in range(5):
env = Cifar10VGG16()
done, state = env.get()
agent = Agent(env.state_size, env.action_size)
while not done:
action = agent.get_action(state)
action = np.where(action > 0.5, 1, 0)
action, reward, done, new_state = env.step(action)
agent.append_sample(state, action, reward)
print('State {}: Reward {}'.format(env._current_state - 1, reward))
state = new_state
if done:
agent.train_model()
agent.model.save_weights('./saved_model/pruning_agent.h5')
_episode = int(_a)
_scores.append(float(_b))
_n_games = _episode + 5000
_agent = Agent((8,), 4)
if os.path.exists(_f_checkpoint):
_agent.net.load_checkpoint(_f_checkpoint)
_writer = SummaryWriter(_d_log)
_is_quit = False
while _episode < _n_games:
_observation = _env.reset()
_done = False
_score = 0.0
while not _done:
_action = _agent.get_action(_observation)
_next_observation, _reward, _done, _info = _env.step(_action)
_score += _reward
_agent.learn(_observation, _reward, _next_observation, _done)
_observation = _next_observation
_rgb = _env.render("rgb_array")
_bgr = cv2.cvtColor(_rgb, cv2.COLOR_RGB2BGR)
cv2.imshow("frame", _bgr)
_key_code = cv2.waitKey(1)
if _key_code in [27, ord('q')]:
_is_quit = True
break
if _is_quit:
break
_scores.append(_score)
