def main(test_episodes=20, test_steps=50):
env = env_search_control()
print(env.observation_space)
print(env.action_space)
act = deepq.learn(env,
network=models.mlp(num_layers=1, num_hidden=64),
total_timesteps=0,
total_episodes=0,
total_steps=0,
load_path="assembly_model_fuzzy_final.pkl")
episode_rewards = []
episode_states = []
for i in range(test_episodes):
obs, done = env.reset()
episode_rew = 0
episode_obs = []
logger.info(
"================== The {} episode start !!! ===================".
format(i))
for j in range(test_steps):
obs, rew, done, _ = env.step(act(obs[None])[0], j)
episode_rew += rew
episode_obs.append(obs)
episode_rewards.append(cp.deepcopy(episode_rew))
episode_states.append(cp.deepcopy(episode_obs))
print("Episode reward", episode_rew)
np.save('../data/test_episode_reward_fuzzy_final_new', episode_rewards)
np.save('../data/test_episode_state_fuzzy_final_new', episode_states)
def search(arg):
env = env_search_control()
# pull_finish = env.pull_peg_up()
# force, state, pull_terminal = env.search_reset()
#
# # position control
# if pull_terminal:
# done = env.pos_control()
state, obs, done = env.reset()
print('force', state[:6])
print('state', state[6:])
Force, State = [], []
# force control
if done:
for i in range(arg['steps']):
current_state = env.get_state()
force, state = current_state[:6], current_state[6:]
Force.append(cp.deepcopy(force))
State.append(cp.deepcopy(state))
_, _, finish = env.step(0, i)
if finish:
break
pull_finish = env.pull_search_peg()
np.save('../data/search_force', Force)
np.save('../data/search_state', State)
def __init__(self, hyperparams):
"""
Initialize agent.
Args:
hyperparams: Dictionary of hyperparameters.
init_node: Whether or not to initialize a new ROS node.
"""
config = copy.deepcopy(AGENT)
config.update(hyperparams)
Agent.__init__(self, config)
self._env = env_search_control(step_max=200,
fuzzy=False,
add_noise=False)
self.x0 = self._hyperparams['x0']
self.use_tf = False
self.observations_stale = True
def main():
env = env_search_control()
act = deepq.learn(
env,
network=models.mlp(num_hidden=64, num_layers=1),
lr=1e-3,
total_timesteps=5000,
total_episodes=100,
total_steps=50,
target_network_update_freq=20,
buffer_size=32,
learning_starts=32,
learning_times=10,
exploration_fraction=0.2,
exploration_final_eps=0.01,
print_freq=10,
param_noise=True,
save_path='_fuzzy_noise_final',
load_path='assembly_model_fuzzy_final.pkl'
)
# load_path = 'assembly_model_fuzzy.pkl'
# load_path = 'assembly_model.pkl'
# print("Saving model to assembly_fuzzy_noise.pkl")
act.save("assembly_model_fuzzy_final.pkl")
np.save(
data_path + 'train_states_' + algorithm_name + '_' + noise_type +
file_name, epochs_states)
np.save(
data_path + 'train_times_' + algorithm_name + '_' + noise_type +
file_name, epochs_times)
# # agent save
agent.store(model_path + 'train_model_' + algorithm_name + '_' +
noise_type + file_name)
if __name__ == '__main__':
algorithm_name = 'dyna_nn_ddpg'
env = env_search_control(step_max=200, fuzzy=False, add_noise=False)
data_path = './prediction_data/'
model_path = './prediction_model/'
file_name = '_epochs_5_episodes_100_none_fuzzy'
model_name = './prediction_model/'
learn(network='mlp',
env=env,
data_path=data_path,
model_based=True,
memory_extend=False,
dyna_learning=True,
model_type='mlp',
noise_type='normal_0.2',
file_name=file_name,
model_path=model_path,
model_name=model_name,
parser.add_argument('--steps', type=int, default=200)
parser.add_argument('--memory_size', type=int, default=3000)
parser.add_argument('--data-file', type=str)
parser.add_argument('--lambda', type=float, default=0.6)
parser.add_argument('--meta_step_size', type=float, default=0.00001)
parser.add_argument('--eta', type=float, default=0.01)
parser.add_argument('--loop', type=float, default=1)
parser.add_argument('--noplot', action='store_false', dest='plot')
parser.add_argument('--record-file', type=str)
parser.add_argument('--seed', type=int)
return vars(parser.parse_args())
if __name__ == '__main__':
args = parse_args()
env = env_search_control()
obs, state, _ = env.reset()
epoch_force_pose = []
epoch_action = []
action = np.zeros(6)
for i in range(args['steps']):
next_obs, next_state, reward, done, safe_or_not, execute_action = env.step(
np.array([0., 0, 0., 0., 0., 0.]), i)
epoch_force_pose.append(cp.deepcopy(next_state))
epoch_action.append(cp.deepcopy(execute_action))
if done:
env.pull_peg_up()
