def dqn_test(episode):
env = DoorOpenTaskEnv(resolution=(64, 64))
agent = DQNAgent(name='door_open', dim_img=(64, 64, 3), dim_act=5)
model_path = os.path.join(sys.path[0], 'saved_models', agent.name,
'models')
agent.dqn_active = tf.keras.models.load_model(model_path)
steps = env.max_episode_steps
start_time = time.time()
success_counter = 0
episodic_returns = []
sedimentary_returns = []
ep_rew = 0
agent.epsilon = 0.0
for ep in range(episode):
obs, info = env.reset()
ep_rew = 0
img = obs.copy()
for st in range(steps):
act = agent.epsilon_greedy(img)
obs, rew, done, info = env.step(act)
img = obs.copy()
ep_rew += rew
if done:
break
# log infomation for each episode
episodic_returns.append(ep_rew)
sedimentary_returns.append(sum(episodic_returns) / (ep + 1))
if env.open:
success_counter += 1
rospy.loginfo(
"\n================\nEpisode: {} \nEpisodeLength: {} \nEpisodeTotalRewards: {} \nAveragedTotalReward: {} \nSuccess: {} \nTime: {} \n================\n"
.format(ep + 1, st + 1, ep_rew, sedimentary_returns[-1],
success_counter,
time.time() - start_time))
model_saver = ModelSaver(500)
# use tensorboard
train_loss = tf.keras.metrics.Mean('train_loss', dtype=tf.float32)
summary_writer = tf.summary.create_file_writer(model_path)
summary_writer.set_as_default()
start_time = time.time()
for ep in range(num_episodes):
obs, info = env.reset()
ep_rew = 0
img = obs.copy()
agent_p.linear_epsilon_decay(curr_ep=ep)
for st in range(num_steps):
act = agent_p.epsilon_greedy(img)
obs, rew, done, info = env.step(act)
nxt_img = obs.copy()
ep_rew += rew
step_counter += 1
# store transition
agent_p.replay_buffer.store(img, act, rew, done, nxt_img)
# train one step
if ep >= agent_p.warmup_episodes:
if not step_counter % train_freq:
for _ in range(train_freq):
agent_p.train_one_step(train_loss)
# finish step, EXTREMELY IMPORTANT!!!
img = nxt_img.copy()
logging.debug(
"\n-\nepisode: {}, step: {} \naction: {} \nreward: {} \ndone: {}"