def train_policy(num_of_envs, log_relative_path, maximum_episode_length,
skip_frame, seed_num, sac_config, total_time_steps,
validate_every_timesteps, task_name):
def _make_env(rank):
def _init():
task = generate_task(task_generator_id=task_name)
env = CausalWorld(task=task,
skip_frame=skip_frame,
enable_visualization=False,
seed=seed_num + rank,
max_episode_length=maximum_episode_length)
env = HERGoalEnvWrapper(env)
return env
set_global_seeds(seed_num)
return _init
os.makedirs(log_relative_path)
env = SubprocVecEnv([_make_env(rank=i) for i in range(num_of_envs)])
model = HER('MlpPolicy',
env,
SAC,
verbose=1,
policy_kwargs=dict(layers=[256, 256, 256]),
**sac_config)
save_config_file(sac_config,
_make_env(0)(),
os.path.join(log_relative_path, 'config.json'))
for i in range(int(total_time_steps / validate_every_timesteps)):
model.learn(total_timesteps=validate_every_timesteps,
tb_log_name="sac",
reset_num_timesteps=False)
model.save(os.path.join(log_relative_path, 'saved_model'))
return
def main(argv):
numControlledJoints = 6
fixed = False
normalize_observations = False
gamma = 0.9
batch_size = 16
memory_limit = 1000000
normalize_returns = True
timesteps = 1000000
policy_name = "reaching_policy"
discreteAction = 0
rend = False
kukaenv = kukaReachGymEnvHer(urdfRoot=robot_data.getDataPath(),
renders=rend,
useIK=0,
isDiscrete=discreteAction,
numControlledJoints=numControlledJoints,
fixedPositionObj=fixed,
includeVelObs=True)
kukaenv = Monitor(kukaenv, log_dir, allow_early_resets=True)
n_actions = kukaenv.action_space.shape[-1]
param_noise = None
action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions),
sigma=float(0.5) *
np.ones(n_actions))
model_class = DDPG
goal_selection_strategy = 'future'
model = HER(CustomPolicy,
kukaenv,
model_class,
n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
verbose=1,
tensorboard_log=
"../pybullet_logs/kuka_reach_ddpg/reaching_DDPG_HER_PHASE",
buffer_size=1000000,
batch_size=64,
random_exploration=0.3,
action_noise=action_noise)
print(colored("-----Timesteps:", "red"))
print(colored(timesteps, "red"))
print(colored("-----Number Joints Controlled:", "red"))
print(colored(numControlledJoints, "red"))
print(colored("-----Object Position Fixed:", "red"))
print(colored(fixed, "red"))
print(colored("-----Policy Name:", "red"))
print(colored(policy_name, "red"))
print(colored("------", "red"))
print(colored("Launch the script with -h for further info", "red"))
model.learn(total_timesteps=timesteps, log_interval=100, callback=callback)
print("Saving model to kuka.pkl")
model.save("../pybullet_logs/kukareach_ddpg_her/" + policy_name)
del model # remove to demonstrate saving and loading
def main(load_policy=True):
global log_dir
model_class = TD3 # works also with SAC and DDPG
action_space = 6
gamma = 0.9
memory_limit = 1000000
timesteps = 15000000
discreteAction = 0
rend = False
# learning rate
env = bioEnv()
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/bioEnv_TD3", buffer_size=1000000,batch_size= 256,
random_exploration=0.3, action_noise=action_noise)
if (load_policy):
model = HER.load("models/TD3/curriculum/best_model_part_11_10g_TRUE.pkl", env=env, n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
tensorboard_log="../pybullet_logs/bioEnv_TD3",
buffer_size=1000000,batch_size=256,random_exploration=0.3, action_noise=action_noise)
model.learn(timesteps,log_interval=100, callback = callback)
model.save("policy_TD3_Discr")
def main(
training_env: PSMCartesianHERDDPGEnv,
eval_env: PSMCartesianHERDDPGEnv = None,
log_dir='./.logs/results'
):
os.makedirs(log_dir, exist_ok=True)
# training_env = Monitor(training_env, log_dir)
n_actions = training_env.action_space.shape[0]
noise_std = 0.2
# Currently using OU noise
action_noise = OrnsteinUhlenbeckActionNoise(
mean=np.zeros(n_actions),
sigma=noise_std * np.ones(n_actions)
)
model_class = DDPG # works also with SAC, DDPG and TD3
rl_model_kwargs = {
'actor_lr': 1e-3,
'critic_lr': 1e-3,
'action_noise': action_noise,
'nb_train_steps': 300,
'nb_rollout_steps': 100,
'gamma': 0.95,
'observation_range': (-1.5,
1.5),
'random_exploration': 0.05,
'normalize_observations': True,
'critic_l2_reg': 0.01
}
# Available strategies (cf paper): future, final, episode, random
model = HER(
'MlpPolicy',
training_env,
model_class,
verbose=1,
n_sampled_goal=4,
goal_selection_strategy='future',
buffer_size=int(1e5),
batch_size=128,
tensorboard_log="./ddpg_dvrk_tensorboard/",
**rl_model_kwargs
)
# Reset the model
training_env.reset()
# Create callbacks
checkpoint_callback = CheckpointCallback(
save_freq=100000,
save_path="./ddpg_dvrk_tensorboard/"
) # save_path="./.model/model_checkpoint/") #save_freq=100000
# eval_callback = EvalCallback(training_env, best_model_save_path='./ddpg_dvrk_tensorboard/best_model',
# log_path=log_dir, eval_freq=500)
callback = CallbackList([checkpoint_callback]) # , eval_callback])
# Train the model
model.learn(4000000, log_interval=100, callback=callback)
model.save("./her_robot_env")
def test_model_manipulation(model_class, goal_selection_strategy):
env = BitFlippingEnv(N_BITS,
continuous=model_class in [DDPG, SAC],
max_steps=N_BITS)
env = DummyVecEnv([lambda: env])
model = HER('MlpPolicy',
env,
model_class,
n_sampled_goal=3,
goal_selection_strategy=goal_selection_strategy,
verbose=0)
model.learn(1000)
model_predict(model, env, n_steps=100, additional_check=None)
model.save('./test_her')
del model
# NOTE: HER does not support VecEnvWrapper yet
with pytest.raises(AssertionError):
model = HER.load('./test_her', env=VecNormalize(env))
model = HER.load('./test_her')
# Check that the model raises an error when the env
# is not wrapped (or no env passed to the model)
with pytest.raises(ValueError):
model.predict(env.reset())
env_ = BitFlippingEnv(N_BITS,
continuous=model_class in [DDPG, SAC],
max_steps=N_BITS)
env_ = HERGoalEnvWrapper(env_)
model_predict(model, env_, n_steps=100, additional_check=None)
model.set_env(env)
model.learn(1000)
model_predict(model, env_, n_steps=100, additional_check=None)
assert model.n_sampled_goal == 3
del model
env = BitFlippingEnv(N_BITS,
continuous=model_class in [DDPG, SAC],
max_steps=N_BITS)
model = HER.load('./test_her', env=env)
model.learn(1000)
model_predict(model, env_, n_steps=100, additional_check=None)
assert model.n_sampled_goal == 3
if os.path.isfile('./test_her.pkl'):
os.remove('./test_her.pkl')
def main(load_policy=False):
global log_dir
model_class = TD3 # works also with SAC and DDPG
action_space = 7
normalize_observations = False
gamma = 0.9
memory_limit = 1000000
normalize_returns = True
timesteps = 8000000
rend = False
obj_pose_rnd_std = 0
env = pandaPushGymGoalEnv(renders=rend,
use_IK=0,
numControlledJoints=action_space,
obj_pose_rnd_std=obj_pose_rnd_std,
includeVelObs=True)
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/TD3_phase1_target_fixed",
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/TD3_phase1_target_fixed",
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/TD3_phase1_target_fixed")
def main():
model_class = DDPG # works also with SAC and DDPG
# -j
action_space = 7
# -p
fixed = True
# -o
normalize_observations = False
# -g
gamma = 0.9
# -b
#batch_size = 16
# -m
memory_limit = 1000000
# -r
normalize_returns = True
# -t
timesteps = 1000000
policy_name = "pushing_policy"
discreteAction = 0
rend = False
env = pandaPushGymEnvHERRand(urdfRoot=robot_data.getDataPath(),
renders=rend,
useIK=0,
isDiscrete=discreteAction,
action_space=action_space,
fixedPositionObj=fixed,
includeVelObs=True)
# Available strategies (cf paper): future, final, episode, random
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.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(
CustomPolicy,
env,
model_class,
n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
verbose=1,
tensorboard_log=
"../pybullet_logs/panda_push_ddpg/stable_baselines/DDPG+HER_FIXED_DYN_RAND",
buffer_size=1000000,
batch_size=256,
random_exploration=0.3,
action_noise=action_noise)
# Train the model starting from a previous policy
model.learn(timesteps)
print("Saving Policy")
model.save("../policies/pushing_fixed_HER_Dyn_Rand")
def launchAgent(model_name: str):
"""
:param model_name: 실행시킬 모델의 종류. HER, DDPG, PPO2 혹은 기타값(DQN)이어야 함
현재는 의도상 PPO2로 세팅할 것
:return: 1000회의 사이클을 돌고 난 이후의 모델
"""
import Reinforcement_AI.env.e_enhanced_image_env as image_env
from stable_baselines import DQN, HER, DDPG, PPO2
from stable_baselines.common import make_vec_env
print("Current Env is " + model_name)
if model_name == "HER":
env = image_env.DetailedMiniMapEnv()
model = HER("CnnPolicy", env=env, model_class=DQN)
if model_name == "DDPG":
env = image_env.DDPGImageEnv()
model = DDPG(policy="CnnPolicy", env=env, normalize_observations=True)
if model_name == "PPO2":
env = make_vec_env(image_env.DetailedMiniMapEnv, n_envs=1)
model = PPO2(policy="CnnPolicy", env=env, verbose=1)
else:
env = image_env.DetailedMiniMapEnv()
model = DQN(
"CnnPolicy", # policy
env=env, # environment
double_q=True, # Double Q enable
prioritized_replay=True, # Replay buffer enabled
verbose=0 # log print
)
for i in range(1000):
if i != 0:
if model_name == "HER":
model = HER.load("detailedmap_HER_" + str(i), env)
if model_name == "DDPG":
model = DDPG.load("detailedmap_DDPG_" + str(i), env)
if model_name == "PPO2":
model = PPO2.load("detailedmap_PPO2_" + str(i), env)
else:
model = DQN.load("detailedmap_DQN_" + str(i), env)
# print('model learn start')
model.learn(total_timesteps=12500) #FPS가 130이상 넘어갈때의 최소수치
print("this model is : detailedmap_" + model_name + "_" + str(i + 1))
# print('model learn finished')
# print('model save start')
model.save("detailedmap_" + model_name + "_" + str(i + 1))
del model
# print('model save end')
return model
def heralgorithm():
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.FUTURE
# Wrap the model
model = HER('MlpPolicy',
env1,
DDPG,
n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
verbose=1)
# Train the model
model.learn(1000)
model.save("./her_bit_env")
def train_HER(env_train, model_name, timesteps=50000):
start = time.time()
n_sampled_goal = 4
goal_selection_strategy = 'future'
model = HER('MlpPolicy',
env_train,
model_class=SAC,
verbose=0,
n_sampled_goal=n_sampled_goal,
goal_selection_strategy=goal_selection_strategy)
model.learn(total_timesteps=timesteps)
end = time.time()
model.save(f"{config.TRAINED_MODEL_DIR}/{model_name}")
print('Training time (HER): ', (end - start) / 60, ' minutes')
return model
def main(env):
n_actions = env.action_space.shape[0]
noise_std = 0.2
# Currently using OU noise
action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions),
sigma=noise_std *
np.ones(n_actions))
model_class = DDPG # works also with SAC, DDPG and TD3
rl_model_kwargs = {
'actor_lr': 1e-3,
'critic_lr': 1e-3,
'action_noise': action_noise,
'nb_train_steps': 300,
'nb_rollout_steps': 100,
'gamma': 0.95,
'observation_range': (-1.5, 1.5),
'random_exploration': 0.05,
'normalize_observations': True,
'critic_l2_reg': 0.01
}
# Available strategies (cf paper): future, final, episode, random
model = HER('MlpPolicy',
env,
model_class,
verbose=1,
n_sampled_goal=4,
goal_selection_strategy='future',
buffer_size=int(1e5),
batch_size=128,
tensorboard_log="./ddpg_dvrk_tensorboard/",
**rl_model_kwargs)
# Reset the model
env.reset()
# Train the model
model.learn(4000000,
log_interval=100,
callback=CheckpointCallback(
save_freq=100000, save_path="./ddpg_dvrk_tensorboard/"))
model.save("./her_robot_env")
def main(load_policy=False):
global log_dir, log_dir_policy
if (load_policy):
log_dir_policy = '../policies/PUSHING_TD3+HER_FIXED_POSITION_DYN_RAND_FROM_FIXED_PHYSICS'
model_class = TD3 # works also with SAC and DDPG
action_space = 7
fixed = True
normalize_observations = False
gamma = 0.9
memory_limit = 1000000
normalize_returns = True
timesteps = 1500000
discreteAction = 0
rend = False
env = pandaPushGymEnvHERRand(urdfRoot=robot_data.getDataPath(), renders=rend, useIK=0,
isDiscrete=discreteAction, action_space = action_space,
fixedPositionObj = fixed, includeVelObs = True)
env = Monitor(env, log_dir, allow_early_resets=True)
# Available strategies (cf paper): future, final, episode, random
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.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(CustomPolicy, env, model_class, n_sampled_goal=4, goal_selection_strategy=goal_selection_strategy,
verbose=1,tensorboard_log="../pybullet_logs/panda_push_TD3/stable_baselines/TD3+HER_FIXED_DYN_RAND", 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/TD3+HER_FIXED_DYN_RAND_FROM_FIXED_PHYSICS",
buffer_size=1000000,batch_size=256,random_exploration=0.3, action_noise=action_noise)
# Train the model starting from a previous policy
model.learn(timesteps, callback = callback )
model.save("../policies/PUSHING_FIXED_TD3_DYN_RAND")
print("Finished train1")
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")
policy_kwargs=dict(layers=[256, 256, 256]))
# DDPG Hyperparams:
# NOTE: it works even without action noise
# n_actions = env.action_space.shape[0]
# noise_std = 0.2
# action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=noise_std * np.ones(n_actions))
# model = HER('MlpPolicy', env, DDPG, n_sampled_goal=n_sampled_goal,
# goal_selection_strategy='future',
# verbose=1, buffer_size=int(1e6),
# actor_lr=1e-3, critic_lr=1e-3, action_noise=action_noise,
# gamma=0.95, batch_size=256,
# policy_kwargs=dict(layers=[256, 256, 256]))
model.learn(int(2e5))
model.save('her_sac_highway')
# Load saved model
model = HER.load('her_sac_highway', env=env)
obs = env.reset()
# Evaluate the agent
episode_reward = 0
for _ in range(100):
action, _ = model.predict(obs)
obs, reward, done, info = env.step(action)
env.render()
episode_reward += reward
if done or info.get('is_success', False):
print("Reward:", episode_reward, "Success?",
'param_noise': None,
'action_noise': action_noise,
'normalize_observations': normalize,
'nb_train_steps': nb_train_steps,
'nb_rollout_steps': nb_rollout_steps,
'batch_size': batch_size,
'critic_l2_reg': critic_l2_reg,
'buffer_size': buffer_size,
'random_exploration': random_exploration,
'policy_kwargs': {
'layer_norm': True
},
'logging': suff
}
model = HER('MlpPolicy', env, DDPG, **kwargs)
start = time.time()
model.learn(total_timesteps=total_timesteps, log_interval=1)
if log:
model.save("pkl/{}".format(suff))
print(
"Saved as {0}, trained {1} primitive policy for {2} timesteps in {3}".
format(suff, policy, total_timesteps,
time.strftime('%H:%M:%S', time.gmtime(time.time() - start))))
else:
print("Trained {0} primitive policy for {1} timesteps in {2}".format(
policy, total_timesteps,
time.strftime('%H:%M:%S', time.gmtime(time.time() - start))))
# Available strategies (cf paper): future, final, episode, random
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.FUTURE
# Wrap the model
model = HER('MlpPolicy',
env,
model_class,
n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
verbose=1)
# Train the model
model.learn(500000)
model.save("her_fetch_reach_env")
# def policy(observation, desired_goal):
# # Here you would implement your smarter policy. In this case,
# # we just sample random actions.
# return env.action_space.sample()
# while not done:
# env.render()
# action = policy(obs['observation'], obs['desired_goal'])
# obs, reward, done, info = env.step(action)
# # If we want, we can substitute a goal here and re-compute
# # the reward. For instance, we can just pretend that the desired
# # goal was what we achieved all along.
# substitute_goal = obs['achieved_goal'].copy()
# Available strategies (cf paper): future, final, episode, random
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.FUTURE
# Wrap the model
model = HER('MlpPolicy',
env,
model_class,
n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
verbose=1)
# Train the model
model.learn(100000)
model.save("fetch_reach_sac_her_future_100000")
# def policy(observation, desired_goal):
# # Here you would implement your smarter policy. In this case,
# # we just sample random actions.
# return env.action_space.sample()
# while not done:
# env.render()
# action = policy(obs['observation'], obs['desired_goal'])
# obs, reward, done, info = env.step(action)
# # If we want, we can substitute a goal here and re-compute
# # the reward. For instance, we can just pretend that the desired
# # goal was what we achieved all along.
# substitute_goal = obs['achieved_goal'].copy()
def callback(_locals, _globals):
global n_steps
n_steps += 1
if n_steps % 50000 == 0 or n_steps == 10000:
print('Saving: ', n_steps)
save_path = 'checkpoints/yumi/her/her_{}_task_{}_{}.npy'.format(
name, args.task, n_steps)
os.makedirs(os.path.dirname(save_path), exist_ok=True)
model.save(save_path)
return True
model = HER('MlpPolicy',
env,
model_class=DDPG,
verbose=1,
tensorboard_log=log_dir,
**dict(random_exploration=.2))
model.learn(total_timesteps=total_timesteps, callback=callback)
model.save("her-yumi-{}-final".format(n_steps))
env.save_running_average(log_dir)
obs = env.reset()
for i in range(100):
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
# Available strategies (cf paper): future, final, episode, random
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.FUTURE
# Wrap the model
model = HER('MlpPolicy',
env,
model_class,
n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
verbose=1)
# Train the model
model.learn(100000)
model.save("hand_egg_DDPG_her_random_100000")
# def policy(observation, desired_goal):
# # Here you would implement your smarter policy. In this case,
# # we just sample random actions.
# return env.action_space.sample()
# while not done:
# env.render()
# action = policy(obs['observation'], obs['desired_goal'])
# obs, reward, done, info = env.step(action)
# # If we want, we can substitute a goal here and re-compute
# # the reward. For instance, we can just pretend that the desired
# # goal was what we achieved all along.
# substitute_goal = obs['achieved_goal'].copy()
env = gym.make('PointMass-%d-v1' % num_objs)
n_actions = env.action_space.shape[-1]
stddev = 0.2
action_noise = NormalActionNoise(mean=np.zeros(n_actions),
sigma=0.01 * np.ones(n_actions))
policy = 'MlpPolicy'
args_alg = dict(
random_exploration=0.2,
buffer_size=int(1E6),
batch_size=256,
nb_eval_steps=10,
action_noise=action_noise,
tensorboard_log=logger,
)
model = HER(policy,
env,
model_class,
n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
verbose=1,
**args_alg)
model.learn(int(nIter))
model.save(expDir + "/%s" % np.format_float_scientific(nIter))
#model = HER.load("point1_deter", env=env)
record_her_indep(env, model, expDir, num_files=10, video_len=500)
lambda: env
]) # The algorithms require a vectorized environment to run
num_env = 2
#env = SubprocVecEnv([make_env(env_id, log_dir, i+worker_id) for i in range(num_env)])
model_class = DQN
# Available strategies (cf paper): future, final, episode, random
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.FUTURE
model = HER('MlpPolicy',
env,
model_class,
n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
verbose=1)
model.learn(total_timesteps=1000)
model.save(log_dir + "model")
# WARNING: you must pass an env
# or wrap your environment with HERGoalEnvWrapper to use the predict method
model = HER.load(log_dir + "model", env=env)
#evaluate agent
episodes = 100
ep_r = []
ep_l = []
for e in range(episodes):
obs = env.reset()
total_r = 0.
total_l = 0.
while True:
action, _states = model.predict(obs)
goal_selection_strategy=args.goal_selection_strategy,
verbose=1,
exploration_fraction=args.exploration_fraction,
tensorboard_log=args.tensorboard_log_path + '/' + args.name)
else:
model = DQN(MlpPolicy,
env,
verbose=1,
tensorboard_log='/srv/share/nkannabiran3/DQN/',
double_q=True,
prioritized_replay=True,
prioritized_replay_alpha=0.8,
prioritized_replay_beta0=0.2)
print('learning')
os.mkdir(args.tensorboard_log_path + '/' + args.name)
parser.save_args()
model.learn(total_timesteps=args.num_training_steps,
tb_log_name=args.tensorboard_log_path + '/' + args.name)
model.save(args.name)
# del model # remove to demonstrate saving and loading
# model = DQN.load("deepq_cartpole")
obs = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
if dones:
break
# env.render()
def get_goal(self):
"""Get a random goal depending on the difficulty."""
self.difficulty = np.random.choice(self.difficulties)
return move_cube.sample_goal(difficulty=self.difficulty)
if __name__ == "__main__":
difficulties = np.arange(1, 5)
env = gym.make(
'Example_CubeEnv-v0',
initializer=Initializer(difficulty=difficulties),
action_type=cube_env.ActionType.POSITION,
visualization=False,
)
model_kwargs = {
'ent_coef': 'auto',
'buffer_size': int(1e6),
'gamma': 0.95,
'learning_starts': 1000,
'train_freq': 1
}
model = HER('MlpPolicy', env, SAC, verbose=True, **model_kwargs)
model.learn(int(8e6))
model.save("./hersac_CubeEnv_diffall")
os.system("shutdown now")
import gym
from stable_baselines.common.policies import MlpPolicy
from stable_baselines import DDPG, HER, TD3, SAC
env = gym.make('FetchReach-v1')
#model = DDPG('MlpPolicy', env)
model = HER('MlpPolicy',
env,
DDPG,
goal_selection_strategy='final',
n_sampled_goal=4)
model.learn(50000000)
model.save('./her_fetch_reach')
#model = HER.load('./her_fetch_reach', env=env)
for _ in range(100):
obs = env.reset()
state = None
done = False
_reward = 0
while not done:
env.render()
action, state = model.predict(obs)
obs, reward, done, info = env.step(action)
_reward += reward
print("Reward = {}".format(_reward))
env, _ = load_env(env_name,
core_dir=core_dir,
envs_dir=envs_dir,
xmls_dir=xmls_dir,
return_args_remaining=True)
# Available strategies (cf paper): future, final, episode, random
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.FUTURE
# Wrap the model
model = HER('MlpPolicy',
env,
model_class,
n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
verbose=1)
# Train the model
model.learn(1000)
model.save("./her_bit_env")
# WARNING: you must pass an env
# or wrap your environment with HERGoalEnvWrapper to use the predict method
model = HER.load('./her_bit_env', env=env)
obs = env.reset()
for _ in range(100):
action, _ = model.predict(obs)
obs, reward, done, _ = env.step(action)
if done:
obs = env.reset()
# Available strategies (cf paper): future, final, episode, random
goal_selection_strategy = 'random' # equivalent to GoalSelectionStrategy.FUTURE
# Wrap the model
model = HER('MlpPolicy',
env,
model_class,
n_sampled_goal=4,
goal_selection_strategy=goal_selection_strategy,
verbose=1)
# Train the model
model.learn(10000)
model.save("hand_reach_sac_her_random_10000")
# def policy(observation, desired_goal):
# # Here you would implement your smarter policy. In this case,
# # we just sample random actions.
# return env.action_space.sample()
# while not done:
# env.render()
# action = policy(obs['observation'], obs['desired_goal'])
# obs, reward, done, info = env.step(action)
# # If we want, we can substitute a goal here and re-compute
# # the reward. For instance, we can just pretend that the desired
# # goal was what we achieved all along.
# substitute_goal = obs['achieved_goal'].copy()
goal_selection_strategy='future',
verbose=1,
buffer_size=int(1e6),
learning_rate=0.001,
gamma=0.95,
batch_size=256,
ent_coef='auto',
random_exploration=0.3,
learning_starts=1000,
train_freq=1,
policy_kwargs=dict(layers=[256, 256, 256]),
tensorboard_log="./OpenAI/")
# Train the model
model.learn(int(8e6))
model.save("./model2")
# WARNING: you must pass an env
# or wrap your environment with HERGoalEnvWrapper to use the predict method
model = HER.load('./model2', env=env)
obs = env.reset()
episodes = 0
successes = 0
step = 0
while (episodes < 50):
step += 1
action, _ = model.predict(obs)
obs, reward, done, _ = env.step(action)
env.render()
if done or step > 1000:
goal_selection_strategy = 'future'
n_actions = env.action_space.shape[-1]
stddev = 0.2
action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.01 * np.ones(n_actions))
policy_kwargs = {}
args_alg = dict(
random_exploration=0.3,
buffer_size=int(1E6),
batch_size=256,
nb_eval_steps=10,
#actor_lr=1e-3,
action_noise=action_noise,
policy_kwargs = policy_kwargs
)
model = HER('MlpPolicy', env, model_class, n_sampled_goal=4, goal_selection_strategy=goal_selection_strategy,
verbose=1, **args_alg)
# Train the model
model.learn(50000)
model.save("fetch_trial")
# WARNING: you must pass an env
# or wrap your environment with HERGoalEnvWrapper to use the predict method
#model = HER.load('fetch_trial.zip', env=env)
dirs = os.getcwd()
record_her_indep(env, model, dirs)
def launchAgent():
import Reinforcement_AI.env.d_image_env as image_env
from stable_baselines import DQN, HER, DDPG, PPO2
from stable_baselines.common import make_vec_env
model_name = "PPO2"
if model_name == "HER":
model = HER(
"CnnPolicy",
env=image_env.DetailedMiniMapEnv(),
model_class=DQN
)
if model_name == "DDPG":
model = DDPG(
policy="CnnPolicy",
env=image_env.DDPGImageEnv(),
normalize_observations=True
)
if model_name == "PPO2":
# env = image_env.DetailedMiniMapEnv()
env = make_vec_env(image_env.DetailedMiniMapEnv, n_envs=1)
model = PPO2(
policy="CnnPolicy",
env=env,
verbose=1
)
else:
model = DQN(
"CnnPolicy", # policy
env=image_env.DetailedMiniMapEnv(), # environment
double_q=True, # Double Q enable
prioritized_replay=True, # Replay buffer enabled
verbose=0 # log print
)
for i in range(1000):
if i != 0:
if model_name == "HER":
model = HER.load("detailedmap_HER_" + str(i))
model.set_env(image_env.DetailedMiniMapEnv())
if model_name == "DDPG":
model = DDPG.load("detailedmap_DDPG_" + str(i))
model.set_env(image_env.DDPGImageEnv())
if model_name == "PPO2":
# print('set env')
# ppo2_env = make_vec_env(image_env.DetailedMiniMapEnv, n_envs=1)
# print('get model')
model = PPO2.load("detailedmap_PPO2_" + str(i), env)
# print('set model env')
# model.set_env(ppo2_env)
else:
model = DQN.load("detailedmap_DQN_" + str(i))
model.set_env(image_env.DetailedMiniMapEnv())
# print('model learn start')
model.learn(total_timesteps=3900)
# print('model learn finished')
# print('model save start')
model.save("detailedmap_" + model_name + "_" + str(i+1))
del model
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import PPO2, DQN, HER, DDPG
import synergyenvs
env = gym.make("GraspBoxPybullet-v0")
env.render()
o = env.reset()
# model = PPO2(MlpPolicy, env, verbose=1)
model = HER('MlpPolicy', env, DDPG, n_sampled_goal=4, verbose=1)
model.learn(50000)
model.save("./her_graspbox-1")
env.camera_adjust()
for _ in range(1000):
o = env.reset()
env.render()
action, _states = model.predict(o)
# action = env.action_space.sample()
o, r, done, info = env.step(action)
print(o, r, done, info)
if done:
o = env.reset()
time.sleep(0.2)
env.close()