class Train():
def __init__(self, config):
self.config = config
self.PD_freq = self.config.conf['LLC-frequency']
self.Physics_freq = self.config.conf['Physics-frequency']
self.network_freq = self.config.conf['HLC-frequency']
self.sampling_skip = int(self.PD_freq / self.network_freq)
self.reward_decay = 1.0
self.reward_scale = config.conf['reward-scale']
self.reward_scale = self.reward_scale / float(
self.sampling_skip) # /10.0#normalizing reward to 1
self.max_time_per_train_episode = self.config.conf['max-train-time']
self.max_step_per_train_episode = int(self.max_time_per_train_episode *
self.network_freq)
self.max_time_per_test_episode = self.config.conf['max-test-time'] #16
self.max_step_per_test_episode = int(self.max_time_per_test_episode *
self.network_freq)
self.train_external_force_disturbance = True
if self.train_external_force_disturbance == True:
path_str = 'with_external_force_disturbance/'
else:
path_str = 'without_external_force_disturbance/'
self.test_external_force_disturbance = True
self.env = Valkyrie(
max_time=self.max_time_per_train_episode,
renders=False,
initial_gap_time=0.5,
PD_freq=self.PD_freq,
Physics_freq=self.Physics_freq,
Kp=config.conf['Kp'],
Kd=config.conf['Kd'],
bullet_default_PD=config.conf['bullet-default-PD'],
controlled_joints_list=config.conf['controlled-joints'])
config.conf['state-dim'] = self.env.stateNumber
self.agent = Agent(self.env, self.config)
self.episode_count = 0
self.step_count = 0
self.train_iter_count = 0
self.best_reward = 0
self.best_episode = 0
self.best_train_iter = 0
self.control = Control(self.config, self.env)
# load weight from previous network
# dir_path = 'record/2017_12_04_15.20.44/no_force' # '2017_05_29_18.23.49/with_force'
# create new network
dir_path = 'TRPO/record/' + '3D_push/' + path_str + datetime.now(
).strftime('%Y_%m_%d_%H.%M.%S')
if not os.path.exists(dir_path):
os.makedirs(dir_path)
if not os.path.exists(dir_path + '/saved_actor_networks'):
os.makedirs(dir_path + '/saved_actor_networks')
if not os.path.exists(dir_path + '/saved_critic_networks'):
os.makedirs(dir_path + '/saved_critic_networks')
self.logging = logger(dir_path)
config.save_configuration(dir_path)
config.record_configuration(dir_path)
config.print_configuration()
self.agent.load_weight(dir_path)
self.dir_path = dir_path
self.on_policy_paths = []
self.off_policy_paths = []
self.buffer = ReplayBuffer(self.config.conf['replay-buffer-size'])
self.force = [0, 0, 0]
self.force_chest = [0, 0,
0] # max(0,force_chest[1]-300*1.0 / EXPLORE)]
self.force_pelvis = [0, 0, 0]
def get_single_path(self):
observations = []
next_observations = []
actions = []
rewards = []
actor_infos = []
means = []
logstds = []
dones = []
task_rewards = []
imitation_rewards = []
state = self.env._reset(Kp=self.config.conf['Kp'],
Kd=self.config.conf['Kd'])
self.control.reset() #TODO check
self.episode_count += 1
for step in range(self.max_step_per_train_episode):
self.step_count += 1
if self.train_external_force_disturbance:
if step == self.network_freq or step == 6 * self.network_freq or step == 11 * self.network_freq: # apply force for every 5 second
# f = np.random.normal(0.4, 1.0) * 3000#600 * self.network_freq / 10
f = np.random.uniform(1200, 3000)
theta = np.random.uniform(-math.pi, math.pi)
fx = f * math.cos(theta)
fy = f * math.sin(theta)
self.force = [f, 0, 0]
else:
self.force = [0, 0, 0]
else:
self.force = [0, 0, 0]
state = self.env.getExtendedObservation()
action, actor_info = self.agent.agent.actor.get_action(state)
mean = actor_info['mean']
logstd = actor_info['logstd']
#action = np.clip(action, self.config.conf['actor-output-bounds'][0], self.config.conf['actor-output-bounds'][1])
action = np.array([action]) if len(
np.shape(action)) == 0 else np.array(action)
next_state, reward, terminal, info = self.control.control_step(
action, self.force,
np.zeros((len(self.config.conf['controlled-joints']), )))
# time_step, reward, _, next_state = self.env.step(action)
# height = next_state['height']
# next_state = control.flatten_observation(next_state)
# next_state = next_state.values()[0]
# terminal = time_step.last()
observations.append(state)
actions.append(action)
rewards.append(reward)
# actor_infos.append(actor_info)
means.append(mean)
logstds.append(logstd)
dones.append(terminal)
next_observations.append(next_state)
task_rewards.append(np.array(info['task_reward']))
imitation_rewards.append(np.array(info['imitation_reward']))
if terminal:
break
state = next_state
path = dict(
observations=np.array(observations),
actions=np.array(actions),
rewards=np.array(rewards),
means=means,
logstds=logstds,
dones=dones, #actor_infos=actor_infos,
next_observations=next_observations,
task_rewards=task_rewards,
imitation_rewards=imitation_rewards)
return path
def get_paths(self, num_of_paths=None, prefix='', verbose=True):
if num_of_paths is None:
num_of_paths = self.config.conf['max-path-num']
paths = []
t = time.time()
if verbose:
print(prefix + 'Gathering Samples')
step_count = 0
path_count = 0
while (1):
path = self.get_single_path()
paths.append(path)
step_count += len(path['dones'])
path_count += 1
num_of_paths = path_count
if step_count >= self.config.conf['max-path-step']:
break
if verbose:
print(
'%i paths sampled. %i steps sampled. %i total paths sampled. Total time used: %f.'
% (num_of_paths, step_count, self.episode_count,
time.time() - t))
return paths
def train_paths(self):
self.train_iter_count += 1
self.on_policy_paths = [] # clear
self.off_policy_paths = [] #clear
self.on_policy_paths = self.get_paths()
# self.buffer.add_paths(self.on_policy_paths)
self.off_policy_paths = copy.deepcopy(self.on_policy_paths)
self.train_actor(True)
self.train_critic(True) #TODO train critic before actor?
def train_critic(self, on_policy=True, prefix='', verbose=True):
t = time.time()
if on_policy == True:
paths = copy.deepcopy(self.on_policy_paths)
for path in paths:
path['V_target'] = []
path['Q_target'] = []
rewards = path['rewards']
next_observations = path['next_observations']
dones = path['dones']
r = np.array(rewards)
if dones[-1] == False: # not terminal state
r[-1] = r[
-1] + self.config.conf['gamma'] * self.agent.agent.V(
next_observations[-1]) # bootstrap
#path['returns'] = discount(path['rewards'], self.config.conf['gamma'])
path['returns'] = discount(r, self.config.conf['gamma'])
# print(discount(path['rewards'], self.config.conf['gamma']))
# print(discount(r, self.config.conf['gamma']))
# print(discount(path['rewards'], self.config.conf['gamma'])-discount(r, self.config.conf['gamma']))
observations = np.concatenate(
[path['observations'] for path in paths])
returns = np.concatenate([path['returns'] for path in paths])
observations = np.vstack(observations)
returns = np.vstack(returns)
qloss = 0
vloss = 0
vloss += self.agent.train_V(observations, returns)
print('qloss', qloss, 'vloss', vloss)
else:
paths = copy.deepcopy(self.off_policy_paths)
#paths = self.off_policy_paths
for path in paths:
length = len(path['rewards'])
path['V_target'] = []
path['Q_target'] = []
rewards = path['rewards']
states = path['states']
actions = path['actions']
next_states = path['next_states']
dones = path['dones']
means = path['means']
logstds = path['logstds']
r = np.array(rewards)
if dones[-1][0] == False: # not terminal state
r[-1][0] = r[-1][
0] + self.config.conf['gamma'] * self.agent.agent.V(
next_states[-1]) # bootstrap
V_trace = self.agent.V_trace(states, actions, next_states,
rewards, dones, means, logstds)
path['V_target'] = V_trace
V_target = np.concatenate([path['V_target'] for path in paths])
states = np.concatenate([path['states'] for path in paths])
# print(states)
actions = np.concatenate([path['actions'] for path in paths])
# print(actions)
qloss = 0
vloss = 0
vloss += self.agent.train_V(states, V_target)
print('qloss', qloss, 'vloss', vloss)
if verbose:
print(prefix + 'Training critic network. Total time used: %f.' %
(time.time() - t))
return
def train_actor(self,
on_policy=True,
prefix='',
verbose=True): # whether or not on policy
t = time.time()
stats = dict()
if on_policy == True:
paths = copy.deepcopy(self.on_policy_paths)
length = len(paths)
for path in paths:
rewards = path['rewards']
observations = path['observations']
next_observations = path['next_observations']
dones = path['dones']
path['baselines'] = self.agent.agent.V(path['observations'])
path['returns'] = discount(path['rewards'],
self.config.conf['gamma'])
if not self.config.conf['GAE']:
path['advantages'] = path['returns'] - path['baselines']
else:
b = np.append(path['baselines'], path['baselines'][-1])
deltas = path[
'rewards'] + self.config.conf['gamma'] * b[1:] - b[:-1]
deltas[-1] = path['rewards'][-1] + (
1 -
dones[-1]) * self.config.conf['gamma'] * b[-1] - b[-1]
#path['advantages'] = discount(deltas, self.config.conf['gamma'] * self.config.conf['lambda'])
path['advantages'] = np.squeeze(
self.agent.GAE(observations, next_observations,
rewards, dones))
# print(discount(deltas, self.config.conf['gamma'] * self.config.conf['lambda']))
# print(self.agent.GAE(observations, next_observations, rewards, dones))
# print(discount(deltas, self.config.conf['gamma'] * self.config.conf['lambda'])-np.squeeze(self.agent.GAE(observations, next_observations, rewards, dones)))
if not self.config.conf['use-critic']:
r = np.array(rewards)
path['advantages'] = discount(r, self.config.conf['gamma'])
observations = np.concatenate(
[path['observations'] for path in paths])
actions = np.concatenate([path['actions'] for path in paths])
# rewards = np.concatenate([path['rewards'] for path in paths])
advantages = np.concatenate([path['advantages'] for path in paths])
# actor_infos = np.concatenate([path['actor_infos'] for path in paths])
means = np.concatenate([path['means'] for path in paths])
logstds = np.concatenate([path['logstds'] for path in paths])
# returns = np.concatenate([path['returns'] for path in paths])
if self.config.conf['center-advantage']:
advantages -= np.mean(advantages)
advantages /= (np.std(advantages) + 1e-8)
# advantages = np.vstack(advantages)
#advantages = advantages.reshape(length,1)
self.agent.train_actor_PPO(observations, actions, advantages,
means, logstds)
else:
paths = self.off_policy_paths
off_policy_states = np.concatenate(
[path['states'] for path in paths])
off_policy_actions = np.concatenate(
[path['actions'] for path in paths])
# print(states)
self.agent.train_actor_DPG(off_policy_states, off_policy_actions)
if verbose:
print(prefix + 'Training actor network. Total time used: %f.' %
(time.time() - t))
return stats
def test(self):
total_reward = 0
total_task_reward = 0
total_imitation_reward = 0
for i in range(self.config.conf['test-num']): #
_ = self.env._reset(Kp=self.config.conf['Kp'],
Kd=self.config.conf['Kd'])
self.control.reset()
for step in range(self.max_step_per_test_episode):
state = self.env.getExtendedObservation()
action, actor_info = self.agent.agent.actor.get_action(state)
mean = actor_info['mean']
logstd = actor_info['logstd']
action = mean
#action = np.clip(action, self.config.conf['actor-output-bounds'][0], self.config.conf['actor-output-bounds'][1])
action = np.array([action]) if len(
np.shape(action)) == 0 else np.array(action)
if self.test_external_force_disturbance:
f = self.env.rejectableForce_xy(1.0 / self.network_freq)
if step == 5 * self.network_freq:
if f[1] == 0:
self.force = np.array(
[600.0 * self.network_freq / 10.0, 0.0, 0.0])
else:
self.force = np.array([1.0 * f[1], 0, 0])
print(self.force)
elif step == 11 * self.network_freq:
if f[0] == 0:
self.force = np.array(
[-600.0 * self.network_freq / 10.0, 0.0, 0.0])
else:
self.force = [1.0 * f[0], 0, 0]
print(self.force)
elif step == 17 * self.network_freq:
if f[2] == 0:
self.force = np.array(
[0.0, -800.0 * self.network_freq / 10.0, 0.0])
else:
self.force = [0, 1.0 * f[2], 0]
print(self.force)
elif step == 23 * self.network_freq:
if f[3] == 0:
self.force = np.array(
[0.0, 800.0 * self.network_freq / 10.0, 0.0])
else:
self.force = [0, 1.0 * f[3], 0]
print(self.force)
else:
self.force = [0, 0, 0]
else:
self.force = [0, 0, 0]
next_state, reward, done, info = self.control.control_step(
action, self.force,
np.zeros((len(self.config.conf['controlled-joints']), )))
total_reward += reward
total_task_reward += info['task_reward']
total_imitation_reward += info['imitation_reward']
if done:
break
#self.env.stopRendering()
ave_reward = total_reward / self.config.conf['test-num']
ave_task_reward = total_task_reward / self.config.conf['test-num']
ave_imitation_reward = total_imitation_reward / self.config.conf[
'test-num']
self.agent.save_weight(self.step_count,
self.dir_path + '/latest_network')
if self.best_reward < ave_reward and self.episode_count > self.config.conf[
'record-start-size']:
self.best_episode = self.episode_count
self.best_train_iter = self.train_iter_count
self.best_reward = ave_reward
self.agent.save_weight(self.step_count,
self.dir_path + '/best_network')
episode_rewards = np.array(
[np.sum(path['rewards']) for path in self.on_policy_paths])
episode_task_rewards = np.array(
[np.sum(path['task_rewards']) for path in self.on_policy_paths])
episode_imitation_rewards = np.array([
np.sum(path['imitation_rewards']) for path in self.on_policy_paths
])
print('iter:' + str(self.train_iter_count) + ' episode:' +
str(self.episode_count) + ' step:' + str(self.step_count) +
' Deterministic policy return:' + str(ave_reward) +
' Average return:' + str(np.mean(episode_rewards)))
print('best train_iter', self.best_train_iter, 'best reward',
self.best_reward)
self.logging.add_train('episode', self.episode_count)
self.logging.add_train('step', self.step_count)
self.logging.add_train('ave_reward', ave_reward)
self.logging.add_train('ave_task_reward', ave_task_reward)
self.logging.add_train('ave_imitation_reward', ave_imitation_reward)
self.logging.add_train('logstd', np.squeeze(self.agent.agent.logstd()))
self.logging.add_train('average_return', np.mean(episode_rewards))
self.logging.add_train('task_rewards', np.mean(episode_task_rewards))
self.logging.add_train('imitation_rewards',
np.mean(episode_imitation_rewards))
#self.logging.save_train()
#self.agent.ob_normalize1.save_normalization(self.dir_path) # TODO test observation normalization
#self.agent.ob_normalize2.save_normalization(self.dir_path) # TODO test observation normalization
self.logging.save_train()
class Run():
def __init__(self, config, dir_path):
self.dir_path = dir_path
self.config = config
self.config.load_configuration(dir_path)
self.config.print_configuration()
self.PD_freq = self.config.conf['LLC-frequency']
self.Physics_freq = self.config.conf['Physics-frequency']
self.network_freq = self.config.conf['HLC-frequency']
self.sampling_skip = int(self.PD_freq / self.network_freq)
self.reward_decay = 1.0
self.reward_scale = config.conf['reward-scale']
self.reward_scale = self.reward_scale / float(
self.sampling_skip) # /10.0#normalizing reward to 1
self.max_time = 10 #16
self.max_step_per_episode = int(self.max_time * self.network_freq)
self.env = Valkyrie(
max_time=self.max_time,
renders=False,
initial_gap_time=0.5,
PD_freq=self.PD_freq,
Physics_freq=self.Physics_freq,
Kp=config.conf['Kp'],
Kd=config.conf['Kd'],
bullet_default_PD=config.conf['bullet-default-PD'],
controlled_joints_list=config.conf['controlled-joints'],
logFileName=dir_path)
config.conf['state-dim'] = self.env.stateNumber
self.agent = Agent(self.env, self.config)
self.agent.load_weight(dir_path + '/best_network')
self.logging = logger(dir_path)
self.episode_count = 0
self.step_count = 0
self.train_iter_count = 0
self.best_reward = 0
self.best_episode = 0
self.best_train_iter = 0
self.control = Control(self.config, self.env)
# create new network
self.force = [0, 0, 0]
self.force_chest = [0, 0,
0] # max(0,force_chest[1]-300*1.0 / EXPLORE)]
self.force_pelvis = [0, 0, 0]
# img = [[1,2,3]*50]*100
img = np.zeros((240, 320, 3))
self.image = plt.imshow(img, interpolation='none', animated=True)
self.ax = plt.gca()
plt.axis('off')
self.image_list = []
def test(self):
total_reward = 0
for i in range(self.config.conf['test-num']): #
_ = self.env._reset(Kp=self.config.conf['Kp'],
Kd=self.config.conf['Kd'],
base_pos_nom=[0, 0, 1.5],
fixed_base=True)
# self.env._setupCamera()
self.env.startRendering()
self.env._startLoggingVideo()
self.control.reset(w_imitation=0.5, w_task=0.5)
for step in range(self.max_step_per_episode):
# self.env._setupCamera()
t = time.time()
state = self.env.getExtendedObservation()
action, actor_info = self.agent.agent.actor.get_action(state)
mean = actor_info['mean']
logstd = actor_info['logstd']
action = mean
# action = np.clip(action, self.config.conf['actor-output-bounds'][0],
# self.config.conf['actor-output-bounds'][1])
action = np.array([action]) if len(
np.shape(action)) == 0 else np.array(action)
f = self.env.rejectableForce_xy(1.0 / self.network_freq)
rgb = self.env._render()
print(rgb.shape)
self.image_list.append(rgb)
# self.image.set_data(rgb)
# self.ax.plot([0])
# plt.pause(0.005)
# if step == 5 * self.network_freq:
# if f[1] == 0:
# self.force = np.array([600.0 * self.network_freq / 10.0, 0.0, 0.0])
# else:
# self.force = np.array([1.0 * f[1], 0, 0])
# print(self.force)
# elif step == 11 * self.network_freq:
# if f[0] == 0:
# self.force = np.array([-600.0 * self.network_freq / 10.0, 0.0, 0.0])
# else:
# self.force = [1.0 * f[0], 0, 0]
# print(self.force)
# elif step == 17 * self.network_freq:
# if f[2] == 0:
# self.force = np.array([0.0, -800.0 * self.network_freq / 10.0, 0.0])
# else:
# self.force = [0, 1.0 * f[2], 0]
# print(self.force)
# elif step == 23 * self.network_freq:
# if f[3] == 0:
# self.force = np.array([0.0, 800.0 * self.network_freq / 10.0, 0.0])
# else:
# self.force = [0, 1.0 * f[3], 0]
# print(self.force)
# else:
# self.force = [0, 0, 0]
next_state, reward, done, info = self.control.control_step(
action, self.force,
np.zeros((len(self.config.conf['controlled-joints']), )))
total_reward += reward
ob = self.env.getObservation()
ob_filtered = self.env.getFilteredObservation()
for l in range(len(ob)):
self.logging.add_run('observation' + str(l), ob[l])
self.logging.add_run('filtered_observation' + str(l),
ob_filtered[l])
self.logging.add_run('action', action)
readings = self.env.getExtendedReading()
for key, value in readings.items():
self.logging.add_run(key, value)
self.logging.add_run('task_reward', info['task_reward'])
self.logging.add_run('imitation_reward',
info['imitation_reward'])
self.logging.add_run('total_reward', info['total_reward'])
#
# while 1:
# if(time.time()-t)>1.0/self.network_freq:
# break
if done:
break
self.env._stopLoggingVideo()
self.env.stopRendering()
clip = ImageSequenceClip(self.image_list, fps=25)
clip.write_gif(self.dir_path + '/test.gif')
clip.write_videofile(self.dir_path + '/test.mp4', fps=25, audio=False)
ave_reward = total_reward / self.config.conf['test-num']
print(ave_reward)
self.logging.save_run()
class Run():
def __init__(self, config, dir_path):
self.dir_path = dir_path
self.config = config
self.config.load_configuration(dir_path)
self.config.print_configuration()
self.PD_freq = self.config.conf['LLC-frequency']
self.Physics_freq = self.config.conf['Physics-frequency']
self.network_freq = self.config.conf['HLC-frequency']
self.sampling_skip = int(self.PD_freq / self.network_freq)
self.reward_decay = 1.0
self.reward_scale = config.conf['reward-scale']
self.reward_scale = self.reward_scale / float(
self.sampling_skip) # /10.0#normalizing reward to 1
self.max_time = 10 #16
self.max_step_per_episode = int(self.max_time * self.network_freq)
self.env = Valkyrie(
max_time=self.max_time,
renders=False,
initial_gap_time=0.5,
PD_freq=self.PD_freq,
Physics_freq=self.Physics_freq,
Kp=config.conf['Kp'],
Kd=config.conf['Kd'],
bullet_default_PD=config.conf['bullet-default-PD'],
controlled_joints_list=config.conf['controlled-joints'],
logFileName=dir_path,
isEnableSelfCollision=False)
config.conf['state-dim'] = self.env.stateNumber + 2
self.agent = Agent(self.env, self.config)
self.agent.load_weight(dir_path + '/best_network')
self.logging = logger(dir_path)
self.episode_count = 0
self.step_count = 0
self.train_iter_count = 0
self.best_reward = 0
self.best_episode = 0
self.best_train_iter = 0
self.control = Control(self.config, self.env)
# create new network
self.force = [0, 0, 0]
self.force_chest = [0, 0,
0] # max(0,force_chest[1]-300*1.0 / EXPLORE)]
self.force_pelvis = [0, 0, 0]
# img = [[1,2,3]*50]*100
img = np.zeros((240, 320, 3))
self.image = plt.imshow(img, interpolation='none', animated=True)
self.ax = plt.gca()
plt.axis('off')
self.image_list = []
self.ref_motion = Motion(config=self.config, dsr_gait_freq=0.6)
def test(self):
total_reward = 0
for i in range(self.config.conf['test-num']): #
# _ = self.env._reset(Kp=self.config.conf['Kp'], Kd=self.config.conf['Kd'], base_pos_nom=[0,0,1.5], fixed_base=True)
state = self.env._reset(Kp=self.config.conf['Kp'],
Kd=self.config.conf['Kd'],
base_pos_nom=[0, 0, 1.175],
fixed_base=False)
q_nom = self.ref_motion.ref_motion_dict()
base_orn_nom = self.ref_motion.get_base_orn()
# state = self.env._reset(Kp=self.config.conf['Kp'], Kd=self.config.conf['Kd'], q_nom=q_nom, base_orn_nom=base_orn_nom, base_pos_nom=[0, 0, 1.175], fixed_base=False)
# self.env._setupCamera()
self.env.startRendering()
self.env._startLoggingVideo()
self.ref_motion.reset(index=0)
# self.ref_motion.random_count()
self.control.reset(
w_imitation=self.config.conf['imitation-weight'],
w_task=self.config.conf['task-weight'])
for step in range(self.max_step_per_episode):
# self.env._setupCamera()
t = time.time()
gait_phase = self.ref_motion.count / self.ref_motion.dsr_length
ref_angle = self.ref_motion.ref_joint_angle()
ref_vel = self.ref_motion.ref_joint_vel()
self.env.checkSelfContact()
state = self.env.getExtendedObservation()
state = np.squeeze(state)
state = np.append(state, [
np.sin(np.pi * 2 * gait_phase),
np.cos(np.pi * 2 * gait_phase)
])
# state = np.append(state,[0,0])
action, actor_info = self.agent.agent.actor.get_action(state)
mean = actor_info['mean']
logstd = actor_info['logstd']
action = mean
# action = np.clip(action, self.config.conf['actor-output-bounds'][0],
# self.config.conf['actor-output-bounds'][1])
action = np.array([action]) if len(
np.shape(action)) == 0 else np.array(action)
f = self.env.rejectableForce_xy(1.0 / self.network_freq)
rgb = self.env._render()
print(rgb.shape)
self.image_list.append(rgb)
# action = self.control.rescale(ref_action, self.config.conf['action-bounds'],
# self.config.conf['actor-output-bounds'])
self.control.update_ref(ref_angle, ref_vel, [])
next_state, reward, terminal, info = self.control.control_step(
action, self.force, gait_phase)
self.ref_motion.index_count()
total_reward += reward
ob = self.env.getObservation()
ob_filtered = self.env.getFilteredObservation()
# for l in range(len(ob)):
# self.logging.add_run('observation' + str(l), ob[l])
# self.logging.add_run('filtered_observation' + str(l), ob_filtered[l])
self.logging.add_run('action', action)
self.logging.add_run('ref_action', ref_angle)
joint_angle = self.control.get_joint_angle()
self.logging.add_run('joint_angle', joint_angle)
readings = self.env.getExtendedReading()
# for key, value in readings.items():
# self.logging.add_run(key, value)
self.logging.add_run('task_reward', info['task_reward'])
self.logging.add_run('imitation_reward',
info['imitation_reward'])
self.logging.add_run('total_reward', info['total_reward'])
#
# while 1:
# if(time.time()-t)>1.0/self.network_freq:
# break
if terminal:
break
self.env._stopLoggingVideo()
self.env.stopRendering()
clip = ImageSequenceClip(self.image_list, fps=25)
clip.write_gif(self.dir_path + '/test.gif')
clip.write_videofile(self.dir_path + '/test.mp4', fps=25, audio=False)
ave_reward = total_reward / self.config.conf['test-num']
print(ave_reward)
self.logging.save_run()
class Run():
def __init__(self, config, dir_path):
self.dir_path = dir_path
self.config = config
self.config.load_configuration(dir_path)
self.config.print_configuration()
self.PD_freq = self.config.conf['LLC-frequency']
self.Physics_freq = self.config.conf['Physics-frequency']
self.network_freq = self.config.conf['HLC-frequency']
self.sampling_skip = int(self.PD_freq / self.network_freq)
self.reward_decay = 1.0
self.reward_scale = config.conf['reward-scale']
self.reward_scale = self.reward_scale / float(
self.sampling_skip) # /10.0#normalizing reward to 1
self.max_time = 10 #16
self.max_step_per_episode = int(self.max_time * self.network_freq)
self.env = Valkyrie(
max_time=self.max_time,
renders=True,
initial_gap_time=0.1,
PD_freq=self.PD_freq,
Physics_freq=self.Physics_freq,
Kp=config.conf['Kp'],
Kd=config.conf['Kd'],
bullet_default_PD=config.conf['bullet-default-PD'],
controlled_joints_list=config.conf['controlled-joints'],
logFileName=dir_path)
config.conf['state-dim'] = self.env.stateNumber
self.agent = Agent(self.env, self.config)
self.agent.load_weight(dir_path + '/best_network')
self.logging = logger(dir_path)
self.episode_count = 0
self.step_count = 0
self.train_iter_count = 0
self.best_reward = 0
self.best_episode = 0
self.best_train_iter = 0
self.control = Control(self.config, self.env)
# create new network
self.force = [0, 0, 0]
self.force_period = [5 * self.network_freq, 5 * self.network_freq + 3]
self.force_chest = [0, 0,
0] # max(0,force_chest[1]-300*1.0 / EXPLORE)]
self.force_pelvis = [0, 0, 0]
self.image_list = []
def test(self):
total_reward = 0
for i in range(self.config.conf['test-num']): #
_ = self.env._reset(Kp=self.config.conf['Kp'],
Kd=self.config.conf['Kd'],
base_pos_nom=[0, 0, 1.175],
fixed_base=False)
self.env._setupCamera(1.5, 30, -10, [0, 0, 1.])
self.env.startRendering()
self.env._startLoggingVideo()
self.control.reset()
for step in range(self.max_step_per_episode):
state = self.env.getExtendedObservation()
action, actor_info = self.agent.agent.actor.get_action(state)
mean = actor_info['mean']
logstd = actor_info['logstd']
action = mean
# action = np.clip(action, self.config.conf['actor-output-bounds'][0],
# self.config.conf['actor-output-bounds'][1])
action = np.array([action]) if len(
np.shape(action)) == 0 else np.array(action)
f = self.env.rejectableForce_xy(1.0 / self.network_freq)
# if step == 5 * self.network_freq:
# if f[1] == 0:
# self.force = np.array([600.0 * self.network_freq / 10.0, 0.0, 0.0])
# else:
# self.force = np.array([1.0 * f[1], 0, 0])
# print(self.force)
# elif step == 11 * self.network_freq:
# if f[0] == 0:
# self.force = np.array([-600.0 * self.network_freq / 10.0, 0.0, 0.0])
# else:
# self.force = [1.0 * f[0], 0, 0]
# print(self.force)
# elif step == 17 * self.network_freq:
# if f[2] == 0:
# self.force = np.array([0.0, -800.0 * self.network_freq / 10.0, 0.0])
# else:
# self.force = [0, 1.0 * f[2], 0]
# print(self.force)
# elif step == 23 * self.network_freq:
# if f[3] == 0:
# self.force = np.array([0.0, 800.0 * self.network_freq / 10.0, 0.0])
# else:
# self.force = [0, 1.0 * f[3], 0]
# print(self.force)
# else:
# self.force = [0, 0, 0]
if step >= self.force_period[0] and step < self.force_period[1]:
self.force = [-500, 0, 3000]
# if step == 5 * self.network_freq:
# self.force = [3000,0,0]
else:
self.force = [0, 0, 0]
self.env.drawForce(self.force)
# rgb=self.env._render(distance=5, pitch=-30, yaw=52)
# print(rgb.shape)
# self.image_list.append(rgb)
next_state, reward, done, info = self.control.control_step(
action, self.force,
np.zeros((len(self.config.conf['controlled-joints']), )))
total_reward += reward
ob = self.env.getObservation()
ob_filtered = self.env.getFilteredObservation()
for l in range(len(ob)):
self.logging.add_run('observation' + str(l), ob[l])
self.logging.add_run('filtered_observation' + str(l),
ob_filtered[l])
self.logging.add_run('action', action)
readings = self.env.getExtendedReading()
for key, value in readings.items():
self.logging.add_run(key, value)
if done:
break
self.env._stopLoggingVideo()
self.env.stopRendering()
ave_reward = total_reward / self.config.conf['test-num']
print(ave_reward)
self.logging.save_run()