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 __init__(self, config, dir_path):
self.dir_path = dir_path
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.max_time = 6
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, isEnableSelfCollision=False)
config.conf['state-dim'] = self.env.stateNumber+1
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)
# 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 __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 __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)
