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))