def dqn_push_test(episode, model, noise):
env = DoorPushTaskEnv(resolution=(64, 64), cam_noise=noise)
act_dim = env.action_dimension()
agent = DQNAgent(name='door_push', dim_img=(64, 64, 3), dim_act=act_dim)
model_path = os.path.join(sys.path[0], "policy", "door_push", model)
agent.dqn_active = tf.keras.models.load_model(model_path)
agent.epsilon = 0.0
return run_dqn_test(episode, env, agent, 60)
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))
def _load_door_pull_agent(self, pull_policy, pull_model):
if pull_policy == 'ppo':
agent = PPOAgent(env_type='discrete',
dim_obs=(64, 64, 3),
dim_act=self.action_dimension())
model_path = os.path.join(sys.path[0], "policy", "door_pull",
pull_model)
agent.actor.logits_net = tf.keras.models.load_model(model_path)
return agent
else:
agent = DQNAgent(name="door_pull",
dim_img=(64, 64, 3),
dim_act=self.action_dimension())
model_path = os.path.join(sys.path[0], 'policy', "door_pull",
pull_model)
agent.dqn_active = tf.keras.models.load_model(model_path)
agent.epsilon = 0.0 # determinitic action without random choice
return agent
num_episodes = args.max_ep
num_steps = args.max_step
# instantiate env
env = DoorPullAndTraverseEnv(resolution=(64, 64), cam_noise=args.noise)
act_dim = env.action_dimension()
train_freq = 80
# variables
step_counter = 0
success_counter = 0
episodic_returns = []
sedimentary_returns = []
ep_rew = 0
# instantiate agent
agent_p = DQNAgent(name='door_pull_traverse',
dim_img=(64, 64, 3),
dim_act=act_dim)
model_path = os.path.join(sys.path[0], 'saved_models', agent_p.name,
'models')
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