def main(load_policy=False):
global log_dir
model_class = TD3 # works also with SAC and DDPG
action_space = 6
fixed = True
#0 completely fixed, 1 slightly random radius, 2 big random radius,
object_position = 1
normalize_observations = False
gamma = 0.9
memory_limit = 1000000
normalize_returns = True
timesteps = 5000000
discreteAction = 0
rend = False
env = pandaPushGymEnvHER(urdfRoot=robot_data.getDataPath(), renders=rend, useIK=1,
isDiscrete=discreteAction, action_space = action_space,
fixedPositionObj = fixed, includeVelObs = True, object_position=object_position)
env = Monitor(env, log_dir, allow_early_resets=True)
goal_selection_strategy = 'future'
n_actions = env.action_space.shape[-1]
action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions))
# Wrap the model
model = HER(CustomTD3Policy, env, model_class, n_sampled_goal=4, goal_selection_strategy=goal_selection_strategy,
verbose=1,tensorboard_log="../pybullet_logs/panda_push_TD3/stable_baselines/PUSHING_TD3+HER_FIXED_POSITION_PHASE_1_IK", buffer_size=1000000,batch_size=256,
random_exploration=0.3, action_noise=action_noise)
if (load_policy):
model = HER.load("../policies/USEFUL_POLICIES/PUSHING_TD3+HER_FIXED_POSITIONbest_model.pkl", env=env, n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
tensorboard_log="../pybullet_logs/panda_push_TD3/stable_baselines/PUSHING_TD3+HER_FIXED_POSITION_PHASE_1_IK",
buffer_size=1000000,batch_size=256,random_exploration=0.3, action_noise=action_noise)
model.learn(timesteps,log_interval=100, callback = callback)
print("Saving Policy PHASE_1")
model.save("../policies/PUSHING_TD3+HER_FIXED_POSITION_PHASE_1_IK")
batch_size = 16
# -m
memory_limit = 1000000
# -r
normalize_returns = True
# -t
timesteps = 100000
policy_name = "pushing_policy"
discreteAction = 0
rend = True
env = pandaPushGymEnvHER(urdfRoot=robot_data.getDataPath(),
renders=rend,
useIK=0,
isDiscrete=discreteAction,
action_space=action_space,
fixedPositionObj=fixed,
includeVelObs=True,
object_position=0,
test_phase=True)
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.FUTURE
# Wrap the model
model = HER.load("../policies/pushing_DDPG_HER_PHASE_1best_model.pkl", env=env)
obs = env.reset()
for _ in range(10000):
action, _ = model.predict(obs)
obs, reward, done, _ = env.step(action)
if done: