# agent.save()
print('\rEpisode {}\tAverage Score: {:.2f}'.format(
i_episode, np.mean(scores_deque)))
if i_episode % 50 == 0:
agent.save()
print('Save Model\n\rEpisode {}\tAverage Score: {:.2f}'.format(
i_episode, np.mean(scores_deque)))
return scores, actor_losses, critic_losses
obs_config = ObservationConfig()
obs_config.set_all(False)
obs_config.joint_velocities = True
obs_config.joint_forces = True
obs_config.joint_positions = True
action_mode = ActionMode(ArmActionMode.ABS_JOINT_TORQUE)
env = RLBenchEnv(
"ReachTarget",
state_type_list=[
"joint_positions",
"joint_velocities",
# 'left_shoulder_rgb',
# 'right_shoulder_rgb',
'task_low_dim_state',
])
state = env.reset()
action_dim = env.action_space.shape[0]
state_space = env.observation_space
import numpy as np
from natsort import natsorted
from rlbench.environment import Environment
from rlbench.observation_config import ObservationConfig
from rlbench.action_modes import ArmActionMode, ActionMode
from rlbench.tasks import ReachTarget
from data.rlbench_data_collection import collect_demos
obs_config = ObservationConfig()
obs_config.front_camera.rgb = True
obs_config.wrist_camera.rgb = True
obs_config.joint_forces = False
action_mode = ActionMode(ArmActionMode.ABS_EE_POSE_WORLD_FRAME)
# action_mode = ActionMode(ArmActionMode.ABS_JOINT_POSITION)
# action_mode = ActionMode(ArmActionMode.ABS_JOINT_VELOCITY)
env = Environment(action_mode,
obs_config=obs_config,
robot_configuration='ur3robotiq',
headless=False)
import time
time.sleep(3)
demos = collect_demos(env,
ReachTarget,
"hola",
n_iter_per_var=1,
n_demos_per_iter=3)
