def simulate_policy(args):
data = joblib.load(args.file)
if args.deterministic:
print('Using the deterministic version of the policy.')
policy = data['policy']
else:
print('Using the stochastic policy.')
policy = data['exploration_policy']
print("Policy loaded")
env = data['env']
if args.gpu:
set_gpu_mode(True)
policy.cuda()
if isinstance(policy, PyTorchModule):
policy.train(False)
while True:
path = rollout(
env,
policy,
max_path_length=args.H,
animated=True,
# deterministic=args.deterministic,
)
if hasattr(env, "log_diagnostics"):
env.log_diagnostics([path])
logger.dump_tabular()
def experiment(variant):
ptu.set_gpu_mode(variant['gpu'])
env = NormalizedBoxEnv(Pusher2D3DofGoalCompoEnv(**variant['env_params']))
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
n_unintentional = 2
net_size = variant['net_size']
u_qfs = [
NNQFunction(obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=(net_size, net_size))
for _ in range(n_unintentional)
]
# i_qf = AvgNNQFunction(obs_dim=obs_dim,
i_qf = SumNNQFunction(obs_dim=obs_dim,
action_dim=action_dim,
q_functions=u_qfs)
# _i_policy = TanhGaussianPolicy(
u_policies = [
StochasticPolicy(
hidden_sizes=[net_size, net_size],
obs_dim=obs_dim,
action_dim=action_dim,
) for _ in range(n_unintentional)
]
i_policy = StochasticPolicy(
hidden_sizes=[net_size, net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
replay_buffer = MultiGoalReplayBuffer(
variant['algo_params']['replay_buffer_size'],
np.prod(env.observation_space.shape), np.prod(env.action_space.shape),
n_unintentional)
variant['algo_params']['replay_buffer'] = replay_buffer
# QF Plot
variant['algo_params']['_epoch_plotter'] = None
algorithm = IUSQL(env=env,
training_env=env,
save_environment=False,
u_qfs=u_qfs,
u_policies=u_policies,
i_policy=i_policy,
i_qf=i_qf,
algo_interface='torch',
min_buffer_size=variant['algo_params']['batch_size'],
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train(online=True)
return algorithm
def simulate_policy(args):
data = joblib.load(args.file)
if args.deterministic:
if args.un > -1:
print(
'Using the deterministic version of the UNintentional policy '
'%02d.' % args.un)
if 'u_policy' in data:
policy = MakeDeterministic(
# MultiPolicySelector(data['u_policy'], args.un))
WeightedMultiPolicySelector(data['policy'], args.un))
else:
policy = MakeDeterministic(
WeightedMultiPolicySelector(data['policy'], args.un))
else:
print('Using the deterministic version of the Intentional policy.')
policy = MakeDeterministic(data['policy'])
else:
if args.un > -1:
print('Using the UNintentional stochastic policy %02d' % args.un)
if 'u_policy' in data:
# policy = MultiPolicySelector(data['u_policy'], args.un)
policy = WeightedMultiPolicySelector(data['policy'], args.un)
else:
# policy = data['u_policies'][args.un]
policy = WeightedMultiPolicySelector(data['policy'], args.un)
else:
print('Using the Intentional stochastic policy.')
# policy = data['exploration_policy']
policy = data['policy']
print("Policy loaded!!")
# Load environment
with open('variant.json') as json_data:
env_params = json.load(json_data)['env_params']
env = NormalizedBoxEnv(Navigation2dGoalCompoEnv(**env_params))
print("Environment loaded!!")
if args.gpu:
set_gpu_mode(True)
policy.cuda()
if isinstance(policy, PyTorchModule):
policy.train(False)
while True:
path = rollout(
env,
policy,
max_path_length=args.H,
animated=True,
# deterministic=args.deterministic,
)
if hasattr(env, "log_diagnostics"):
env.log_diagnostics([path])
logger.dump_tabular()
def experiment(variant):
ptu.set_gpu_mode(variant['gpu'])
env = NormalizedBoxEnv(gym.make(variant['env_name']))
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
net_size = variant['net_size']
qf = NNQFunction(obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size])
policy = TanhMlpPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size],
)
es = OUStrategy(
action_space=env.action_space,
mu=0,
theta=0.15,
max_sigma=0.3,
min_sigma=0.3,
decay_period=100000,
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
replay_buffer = SimpleReplayBuffer(
variant['algo_params']['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
variant['algo_params']['replay_buffer'] = replay_buffer
# QF Plot
# variant['algo_params']['epoch_plotter'] = None
algorithm = DDPG(
explo_env=env,
# training_env=env,
save_environment=False,
policy=policy,
explo_policy=exploration_policy,
qf=qf,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return algorithm
def experiment(variant):
ptu.set_gpu_mode(variant['gpu'])
# env = NormalizedBoxEnv(
# Reacher2D3DofBulletEnv(**variant['env_params'])
# )
env = Reacher2D3DofBulletEnv(**variant['env_params'])
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
initial_conds = [
[10, 5, 20, 0.2, 0.5, 0],
[10, 5, 20, 0.1, 0.1, 0],
[10, 5, 20, 0.15, 0.8, 0],
]
for init_cond in initial_conds:
env.add_initial_condition(robot_config=np.deg2rad(init_cond[:3]),
tgt_state=init_cond[-3:])
net_size = variant['net_size']
# global_policy = TanhGaussianPolicy(
global_policy = MlpPolicy(
hidden_sizes=[net_size, net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
local_policies = [
LinearGaussianPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
T=PATH_LENGTH,
) for _ in range(N_LOCAL_POLS)
]
#
# replay_buffer = FakeReplayBuffer()
# variant['algo_params']['replay_buffer'] = replay_buffer
#
# # QF Plot
# # variant['algo_params']['epoch_plotter'] = None
algorithm = MDGPS(env=env,
eval_env=env,
save_environment=False,
local_policies=local_policies,
global_policy=global_policy,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return algorithm
def experiment(variant):
ptu.set_gpu_mode(variant['gpu'])
goal = variant['env_params'].get('goal')
variant['env_params']['goal_poses'] = \
[goal, (goal[0], 'any'), ('any', goal[1])]
variant['env_params'].pop('goal')
env = NormalizedBoxEnv(Pusher2D3DofMultiGoalEnv(**variant['env_params']))
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
net_size = variant['net_size']
qf = NNQFunction(obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=(net_size, net_size))
if ptu.gpu_enabled():
qf.cuda()
# _i_policy = TanhGaussianPolicy(
policy = SamplingPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size],
)
if ptu.gpu_enabled():
policy.cuda()
replay_buffer = SimpleReplayBuffer(
variant['algo_params']['replay_buffer_size'],
np.prod(env.observation_space.shape),
np.prod(env.action_space.shape),
)
variant['algo_params']['replay_buffer'] = replay_buffer
# QF Plot
variant['algo_params']['_epoch_plotter'] = None
algorithm = SQL(
env=env,
training_env=env,
save_environment=False,
qf=qf,
policy=policy,
# algo_interface='torch',
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train(online=True)
return algorithm
def simulate_policy(args):
data = joblib.load(args.file)
if args.deterministic:
print('Using the deterministic version of the policy.')
policy = data['policy']
else:
print('Using the stochastic policy.')
policy = data['exploration_policy']
# env = data['env']
env = NormalizedBoxEnv(gym.make(args.env))
print("Environment loaded!!")
# # Load environment
# with open('variant.json') as json_data:
# env_params = json.load(json_data)['env_params']
# env_params.pop('goal')
# env_params['is_render'] = True
# env = NormalizedBoxEnv(args.env(**env_params))
# print("Environment loaded!!")
if args.gpu:
set_gpu_mode(True)
policy.cuda()
# else:
# set_gpu_mode(False)
# policy.cpu()
if isinstance(policy, PyTorchModule):
policy.train(False)
while True:
if args.record:
env.start_recording_video('prueba.mp4')
path = rollout(
env,
policy,
max_path_length=args.H,
animated=True,
# deterministic=args.deterministic,
)
print('Accum reward is: ', path['rewards'].sum())
if hasattr(env, "log_diagnostics"):
env.log_diagnostics([path])
logger.dump_tabular()
if args.record:
env.stop_recording_video()
break
def experiment(variant):
ptu.set_gpu_mode(variant['gpu'])
env = NormalizedBoxEnv(gym.make(variant['env_name']))
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
net_size = variant['net_size']
policy = TanhGaussianPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size],
)
replay_buffer = SimpleReplayBuffer(
variant['algo_params']['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
variant['algo_params']['replay_buffer'] = replay_buffer
# QF Plot
# variant['algo_params']['epoch_plotter'] = None
algorithm = Reinforce(
env=env,
# training_env=env,
save_environment=False,
policy=policy,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return algorithm
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
# Set seeds
np.random.seed(variant['seed'])
ptu.set_gpu_mode(variant['gpu'], gpu_id=0)
ptu.seed(variant['seed'])
variant['env_params']['seed'] = variant['seed']
env = NormalizedBoxEnv(
CentauroTrayEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = env.obs_dim
action_dim = env.action_dim
n_unintentional = 2
if variant['load_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
start_epoch = data['epoch']
i_qf = data['qf']
i_qf2 = data['qf2']
u_qf = data['u_qf']
u_qf2 = data['u_qf2']
i_vf = data['i_vf']
u_vf = data['u_vf']
policy = data['policy']
env._obs_mean = data['obs_mean']
env._obs_var = data['obs_var']
else:
start_epoch = 0
net_size = variant['net_size']
u_qf = NNMultiQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
n_qs=n_unintentional,
hidden_activation=variant['hidden_activation'],
# shared_hidden_sizes=[net_size, net_size],
shared_hidden_sizes=[net_size],
# shared_hidden_sizes=[],
unshared_hidden_sizes=[net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
i_qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=variant['hidden_activation'],
hidden_sizes=[net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
if USE_Q2:
u_qf2 = NNMultiQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
n_qs=n_unintentional,
hidden_activation=variant['hidden_activation'],
# shared_hidden_sizes=[net_size, net_size],
shared_hidden_sizes=[net_size],
# shared_hidden_sizes=[],
unshared_hidden_sizes=[net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
i_qf2 = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
else:
u_qf2 = None
i_qf2 = None
if EXPLICIT_VF:
u_vf = NNMultiVFunction(
obs_dim=obs_dim,
n_vs=n_unintentional,
hidden_activation=variant['hidden_activation'],
# shared_hidden_sizes=[net_size, net_size],
shared_hidden_sizes=[net_size],
# shared_hidden_sizes=[],
unshared_hidden_sizes=[net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
i_vf = NNVFunction(
obs_dim=obs_dim,
hidden_sizes=[net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
else:
u_vf = None
i_vf = None
policy = POLICY(
obs_dim=obs_dim,
action_dim=action_dim,
n_policies=n_unintentional,
hidden_activation=variant['hidden_activation'],
# shared_hidden_sizes=[net_size, net_size],
shared_hidden_sizes=[net_size],
# shared_hidden_sizes=[],
unshared_hidden_sizes=[net_size, net_size],
unshared_mix_hidden_sizes=[net_size, net_size],
stds=None,
input_norm=variant['input_norm'],
shared_layer_norm=variant['shared_layer_norm'],
policies_layer_norm=variant['policies_layer_norm'],
mixture_layer_norm=variant['mixture_layer_norm'],
mixing_temperature=1.,
softmax_weights=variant['softmax_weights'],
hidden_w_init=variant['pol_hidden_w_init'],
output_w_init=variant['pol_output_w_init'],
)
if INIT_AVG_MIXING:
set_average_mixing(
policy, n_unintentional, obs_dim,
batch_size=50,
total_iters=1000,
)
replay_buffer = MultiGoalReplayBuffer(
max_replay_buffer_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
reward_vector_size=n_unintentional,
)
algorithm = HIUSAC(
env=env,
policy=policy,
u_qf1=u_qf,
replay_buffer=replay_buffer,
batch_size=BATCH_SIZE,
i_qf1=i_qf,
u_qf2=u_qf2,
i_qf2=i_qf2,
u_vf=u_vf,
i_vf=i_vf,
eval_env=env,
save_environment=False,
**variant['algo_params']
)
if ptu.gpu_enabled():
algorithm.cuda(ptu.device)
# algorithm.pretrain(10000)
algorithm.train(start_epoch=start_epoch)
return algorithm
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
# Set seeds
np.random.seed(variant['seed'])
ptu.set_gpu_mode(variant['gpu'])
ptu.seed(variant['seed'])
variant['env_params']['seed'] = variant['seed']
env = NormalizedBoxEnv(
Pusher2D3DofGoalCompoEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = env.obs_dim
action_dim = env.action_dim
n_unintentional = 2
if variant['load_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
start_epoch = data['epoch']
i_qf = data['qf']
u_qf = data['u_qf']
policy = data['policy']
exploration_policy = data['exploration_policy']
env._obs_mean = data['obs_mean']
env._obs_var = data['obs_var']
else:
start_epoch = 0
net_size = variant['net_size']
u_qf = NNMultiQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
n_qs=n_unintentional,
hidden_activation=variant['hidden_activation'],
# shared_hidden_sizes=[net_size, net_size],
shared_hidden_sizes=[net_size],
# shared_hidden_sizes=[],
unshared_hidden_sizes=[net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
i_qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=variant['hidden_activation'],
hidden_sizes=[net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
policy = POLICY(
obs_dim=obs_dim,
action_dim=action_dim,
n_policies=n_unintentional,
hidden_activation=variant['hidden_activation'],
# shared_hidden_sizes=[net_size, net_size],
shared_hidden_sizes=[net_size],
# shared_hidden_sizes=[],
unshared_hidden_sizes=[net_size, net_size],
unshared_mix_hidden_sizes=[net_size, net_size],
stds=None,
input_norm=variant['input_norm'],
shared_layer_norm=variant['shared_layer_norm'],
policies_layer_norm=variant['policies_layer_norm'],
mixture_layer_norm=variant['mixture_layer_norm'],
mixing_temperature=1.,
softmax_weights=variant['softmax_weights'],
hidden_w_init=variant['pol_hidden_w_init'],
output_w_init=variant['pol_output_w_init'],
)
if INIT_AVG_MIXING:
set_average_mixing(
policy,
n_unintentional,
obs_dim,
batch_size=50,
total_iters=1000,
)
es = OUStrategy(
action_space=env.action_space,
mu=0,
theta=0.15,
max_sigma=0.3,
min_sigma=0.3,
decay_period=100000,
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
replay_buffer = MultiGoalReplayBuffer(
max_replay_buffer_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
reward_vector_size=n_unintentional,
)
algorithm = HIUDDPG(env=env,
policy=policy,
explo_policy=exploration_policy,
u_qf=u_qf,
replay_buffer=replay_buffer,
batch_size=BATCH_SIZE,
i_qf=i_qf,
eval_env=env,
save_environment=False,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
# algorithm.pretrain(PATH_LENGTH*2)
algorithm.train(start_epoch=start_epoch)
return algorithm
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
np.random.seed(SEED)
ptu.set_gpu_mode(variant['gpu'])
ptu.seed(SEED)
env = NormalizedBoxEnv(
CentauroTrayEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
if variant['log_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
raise NotImplementedError
else:
start_epoch = 0
net_size = variant['net_size']
qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size]
)
policy = TanhMlpPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size],
)
es = OUStrategy(
action_space=env.action_space,
mu=0,
theta=0.15,
max_sigma=0.3,
min_sigma=0.3,
decay_period=100000,
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
# Clamp model parameters
qf.clamp_all_params(min=-0.003, max=0.003)
policy.clamp_all_params(min=-0.003, max=0.003)
replay_buffer = SimpleReplayBuffer(
max_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = DDPG(
explo_env=env,
policy=policy,
explo_policy=exploration_policy,
qf=qf,
replay_buffer=replay_buffer,
batch_size=BATCH_SIZE,
eval_env=env,
save_environment=False,
**variant['algo_params']
)
if ptu.gpu_enabled():
algorithm.cuda()
# algorithm.pretrain(PATH_LENGTH*2)
algorithm.train(start_epoch=start_epoch)
return algorithm
def experiment(variant):
render_q = variant['render_q']
save_q_path = '/home/desteban/logs/goalcompo_q_plots'
ptu.set_gpu_mode(variant['gpu'])
env = NormalizedBoxEnv(
Navigation2dGoalCompoEnv(**variant['env_params'])
)
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
n_unintentional = 2
net_size = variant['net_size']
u_qfs = [NNQFunction(obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=(net_size, net_size))
for _ in range(n_unintentional)]
# i_qf = AvgNNQFunction(obs_dim=obs_dim,
i_qf = SumNNQFunction(obs_dim=obs_dim,
action_dim=action_dim,
q_functions=u_qfs)
# _i_policy = TanhGaussianPolicy(
u_policies = [StochasticPolicy(
hidden_sizes=[net_size, net_size],
obs_dim=obs_dim,
action_dim=action_dim,
) for _ in range(n_unintentional)]
i_policy = StochasticPolicy(
hidden_sizes=[net_size, net_size],
obs_dim=obs_dim,
action_dim=action_dim,)
replay_buffer = MultiGoalReplayBuffer(
variant['algo_params']['replay_buffer_size'],
np.prod(env.observation_space.shape),
np.prod(env.action_space.shape),
n_unintentional
)
variant['algo_params']['replay_buffer'] = replay_buffer
# QF Plot
goal_pos = expt_variant['env_params']['goal_position']
q_fcn_positions = [
(goal_pos[0], 0.0),
(0.0, 0.0),
(0.0, goal_pos[1])
]
plotter = QFPolicyPlotter(
i_qf=i_qf,
i_policy=i_policy,
u_qfs=u_qfs,
u_policies=u_policies,
obs_lst=q_fcn_positions,
default_action=[np.nan, np.nan],
n_samples=100,
render=render_q,
save_path=save_q_path,
)
variant['algo_params']['_epoch_plotter'] = plotter
# variant['algo_params']['_epoch_plotter'] = None
algorithm = IUSQL(
env=env,
training_env=env,
save_environment=False,
u_qfs=u_qfs,
u_policies=u_policies,
i_policy=i_policy,
i_qf=i_qf,
algo_interface='torch',
min_buffer_size=variant['algo_params']['batch_size'],
**variant['algo_params']
)
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train()
return algorithm
import argparse
# np.set_printoptions(precision=3, suppress=True)
TEND = 4.0
SIM_TIMESTEP = 0.01
FRAME_SKIP = 1
TS = FRAME_SKIP * SIM_TIMESTEP
T = int(TEND/TS)
GPU = True
# GPU = False
SEED = 450
ptu.set_gpu_mode(GPU)
np.random.seed(SEED)
ptu.seed(SEED)
noise_hyperparams = dict(
smooth_noise=True, # Apply Gaussian filter to noise generated
smooth_noise_var=2.0e+0, # np.power(2*Ts, 2), # Variance to apply to Gaussian Filter. In Kumar (2016) paper, it is the std dev of 2 Ts
smooth_noise_renormalize=True, # Renormalize smooth noise to have variance=1
noise_var_scale=1.e-5*np.array([1., 1., 1., 1., .1, 0.1, 0.1]), # Scale to Gaussian noise: N(0, 1)*sqrt(noise_var_scale), only if smooth_noise_renormalize
)
algo_params = dict(
seed=SEED,
nepochs=100,
num_samples=3,
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
np.random.seed(SEED)
ptu.set_gpu_mode(variant['gpu'])
ptu.seed(SEED)
goal = variant['env_params'].get('goal')
variant['env_params']['goal_poses'] = \
[goal, (goal[0], 'any'), ('any', goal[1])]
variant['env_params'].pop('goal')
env = NormalizedBoxEnv(
Pusher2D3DofGoalCompoEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
if variant['log_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
start_epoch = data['epoch']
qf = data['qf']
policy = data['policy']
env._obs_mean = data['obs_mean']
env._obs_var = data['obs_var']
else:
start_epoch = 0
net_size = variant['net_size']
qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size]
)
policy = POLICY(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size],
)
# Clamp model parameters
qf.clamp_all_params(min=-0.003, max=0.003)
policy.clamp_all_params(min=-0.003, max=0.003)
replay_buffer = SimpleReplayBuffer(
max_replay_buffer_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = PPO(
env=env,
policy=policy,
qf=qf,
# replay_buffer=replay_buffer,
# batch_size=BATCH_SIZE,
eval_env=env,
save_environment=False,
**variant['algo_params']
)
if ptu.gpu_enabled():
algorithm.cuda()
# algorithm.pretrain(PATH_LENGTH*2)
algorithm.train(start_epoch=start_epoch)
return algorithm
def simulate_policy(args):
np.random.seed(SEED)
ptu.seed(SEED)
data = joblib.load(args.file)
if args.deterministic:
if args.un > -1:
print(
'Using the deterministic version of the UNintentional policy '
'%02d.' % args.un)
if 'u_policy' in data:
policy = MakeDeterministic(
MultiPolicySelector(data['u_policy'], args.un))
# WeightedMultiPolicySelector(data['u_policy'], args.un))
else:
# policy = MakeDeterministic(data['u_policies'][args.un])
if isinstance(data['policy'], TanhGaussianPolicy):
policy = MakeDeterministic(data['policy'])
else:
policy = MakeDeterministic(
WeightedMultiPolicySelector(data['policy'], args.un))
else:
print('Using the deterministic version of the Intentional policy.')
if isinstance(data['policy'], ExplorationPolicy):
policy = MakeDeterministic(data['policy'])
else:
policy = data['policy']
else:
if args.un > -1:
print('Using the UNintentional stochastic policy %02d' % args.un)
if 'u_policy' in data:
# policy = MultiPolicySelector(data['u_policy'], args.un)
policy = WeightedMultiPolicySelector(data['u_policy'], args.un)
else:
policy = WeightedMultiPolicySelector(data['policy'], args.un)
# policy = data['policy'][args.un]
else:
print('Using the Intentional stochastic policy.')
# policy = data['exploration_policy']
policy = data['policy']
print("Policy loaded!!")
# Load environment
dirname = os.path.dirname(args.file)
with open(os.path.join(dirname, 'variant.json')) as json_data:
log_data = json.load(json_data)
env_params = log_data['env_params']
H = int(log_data['path_length'])
env_params['is_render'] = True
if 'obs_mean' in data.keys():
obs_mean = data['obs_mean']
print('OBS_MEAN')
print(repr(obs_mean))
else:
obs_mean = None
# obs_mean = np.array([ 0.07010766, 0.37585765, 0.21402615, 0.24426296, 0.5789634 ,
# 0.88510203, 1.6878743 , 0.02656335, 0.03794186, -1.0241051 ,
# -0.5226027 , 0.6198239 , 0.49062446, 0.01197532, 0.7888951 ,
# -0.4857273 , 0.69160587, -0.00617676, 0.08966777, -0.14694819,
# 0.9559917 , 1.0450271 , -0.40958315, 0.86435956, 0.00609685,
# -0.01115279, -0.21607827, 0.9762933 , 0.80748135, -0.48661205,
# 0.7473679 , 0.01649722, 0.15451911, -0.17285274, 0.89978695])
if 'obs_var' in data.keys():
obs_var = data['obs_var']
print('OBS_VAR')
print(repr(obs_var))
else:
obs_var = None
# obs_var = np.array([0.10795759, 0.12807205, 0.9586606 , 0.46407 , 0.8994803 ,
# 0.35167143, 0.30286264, 0.34667444, 0.35105848, 1.9919134 ,
# 0.9462659 , 2.245269 , 0.84190637, 1.5407104 , 0.1 ,
# 0.10330457, 0.1 , 0.1 , 0.1 , 0.1528581 ,
# 0.1 , 0.1 , 0.1 , 0.1 , 0.1 ,
# 0.1 , 0.1 , 0.1 , 0.1 , 0.12320185,
# 0.1 , 0.18369523, 0.200373 , 0.11895574, 0.15118493])
print(env_params)
if args.subtask and args.un != -1:
env_params['subtask'] = args.un
# else:
# env_params['subtask'] = None
env = NormalizedBoxEnv(
CentauroTrayEnv(**env_params),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
print("Environment loaded!!")
if args.gpu:
set_gpu_mode(True)
policy.cuda()
if isinstance(policy, MakeDeterministic):
if isinstance(policy.stochastic_policy, PyTorchModule):
policy.stochastic_policy.train(False)
else:
if isinstance(policy, PyTorchModule):
policy.train(False)
while True:
if args.record:
rollout_start_fcn = lambda: \
env.start_recording_video('centauro_video.mp4')
rollout_end_fcn = lambda: \
env.stop_recording_video()
else:
rollout_start_fcn = None
rollout_end_fcn = None
obs_normalizer = data.get('obs_normalizer')
if args.H != -1:
H = args.H
path = rollout(
env,
policy,
max_path_length=H,
animated=True,
obs_normalizer=obs_normalizer,
rollout_start_fcn=rollout_start_fcn,
rollout_end_fcn=rollout_end_fcn,
)
plot_rollout_reward(path)
if hasattr(env, "log_diagnostics"):
env.log_diagnostics([path])
logger.dump_tabular()
if args.record:
break
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
# Set seeds
np.random.seed(variant['seed'])
ptu.set_gpu_mode(variant['gpu'], gpu_id=0)
ptu.seed(variant['seed'])
variant['env_params']['seed'] = variant['seed']
env = NormalizedBoxEnv(
Reacher2D3DofGoalCompoEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = env.obs_dim
action_dim = env.action_dim
if variant['load_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
start_epoch = data['epoch']
raise NotImplementedError
else:
start_epoch = 0
net_size = variant['net_size']
qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=variant['hidden_activation'],
hidden_sizes=[net_size, net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
policy = POLICY(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=variant['hidden_activation'],
hidden_sizes=[net_size, net_size, net_size],
hidden_w_init=variant['pol_hidden_w_init'],
output_w_init=variant['pol_output_w_init'],
)
es = OUStrategy(
action_space=env.action_space,
mu=0,
theta=0.15,
max_sigma=0.3,
min_sigma=0.3,
decay_period=100000,
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
replay_buffer = SimpleReplayBuffer(
max_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = DDPG(
explo_env=env,
policy=policy,
explo_policy=exploration_policy,
qf=qf,
replay_buffer=replay_buffer,
batch_size=BATCH_SIZE,
eval_env=env,
save_environment=False,
**variant['algo_params']
)
if ptu.gpu_enabled():
algorithm.cuda(ptu.device)
algorithm.pretrain(variant['steps_pretrain'])
algorithm.train(start_epoch=start_epoch)
return algorithm
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
# Set seeds
np.random.seed(variant['seed'])
ptu.set_gpu_mode(variant['gpu'], gpu_id=0)
ptu.seed(variant['seed'])
variant['env_params']['seed'] = variant['seed']
env = NormalizedBoxEnv(
Navigation2dGoalCompoEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = env.obs_dim
action_dim = env.action_dim
if variant['load_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
start_epoch = data['epoch']
qf = data['qf']
qf2 = data['qf2']
vf = data['vf']
policy = data['policy']
env._obs_mean = data['obs_mean']
env._obs_var = data['obs_var']
else:
start_epoch = 0
net_size = variant['net_size']
qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=variant['hidden_activation'],
hidden_sizes=[net_size, net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
if USE_Q2:
qf2 = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=variant['hidden_activation'],
hidden_sizes=[net_size, net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
else:
qf2 = None
if EXPLICIT_VF:
vf = NNVFunction(
obs_dim=obs_dim,
hidden_activation=variant['hidden_activation'],
hidden_sizes=[net_size, net_size, net_size],
hidden_w_init=variant['v_hidden_w_init'],
output_w_init=variant['v_output_w_init'],
)
else:
vf = None
policy = POLICY(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=variant['hidden_activation'],
hidden_sizes=[net_size, net_size, net_size],
hidden_w_init=variant['pol_hidden_w_init'],
output_w_init=variant['pol_output_w_init'],
)
replay_buffer = SimpleReplayBuffer(
max_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = SAC(explo_env=env,
policy=policy,
qf=qf,
qf2=qf2,
vf=vf,
replay_buffer=replay_buffer,
batch_size=BATCH_SIZE,
eval_env=env,
save_environment=False,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda(ptu.device)
algorithm.pretrain(variant['steps_pretrain'])
algorithm.train(start_epoch=start_epoch)
return algorithm
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
# Set seeds
np.random.seed(variant['seed'])
ptu.set_gpu_mode(variant['gpu'], gpu_id=0)
ptu.seed(variant['seed'])
variant['env_params']['seed'] = variant['seed']
env = NormalizedBoxEnv(
Pusher2D3DofGoalCompoEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = env.obs_dim
action_dim = env.action_dim
if variant['load_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
start_epoch = data['epoch']
qf = data['qf']
qf2 = data['qf2']
vf = data['vf']
policy = data['policy']
env._obs_mean = data['obs_mean']
env._obs_var = data['obs_var']
else:
start_epoch = 0
net_size = variant['net_size']
qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=expt_params['hidden_activation'],
hidden_sizes=[net_size, net_size],
)
if USE_Q2:
qf2 = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=expt_params['hidden_activation'],
hidden_sizes=[net_size, net_size],
)
else:
qf2 = None
vf = NNVFunction(
obs_dim=obs_dim,
hidden_activation=expt_params['hidden_activation'],
hidden_sizes=[net_size, net_size],
)
policy = POLICY(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=expt_params['hidden_activation'],
hidden_sizes=[net_size, net_size],
)
# # Clamp model parameters
# qf.clamp_all_params(min=-0.003, max=0.003)
# vf.clamp_all_params(min=-0.003, max=0.003)
# policy.clamp_all_params(min=-0.003, max=0.003)
# if USE_Q2:
# qf2.clamp_all_params(min=-0.003, max=0.003)
replay_buffer = SimpleReplayBuffer(
max_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = SAC(explo_env=env,
policy=policy,
qf=qf,
vf=vf,
replay_buffer=replay_buffer,
batch_size=BATCH_SIZE,
qf2=qf2,
eval_env=env,
save_environment=False,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.pretrain(variant['steps_pretrain'])
algorithm.train(start_epoch=start_epoch)
return algorithm
def simulate_policy(args):
np.random.seed(SEED)
ptu.seed(SEED)
data = joblib.load(args.file)
if args.deterministic:
print('Using the deterministic version of the policy.')
if isinstance(data['policy'], ExplorationPolicy):
policy = MakeDeterministic(data['policy'])
else:
policy = data['policy']
else:
print('Using the stochastic policy.')
policy = data['exploration_policy']
print("Policy loaded!!")
# Load environment
with open('variant.json') as json_data:
env_params = json.load(json_data)['env_params']
env_params['is_render'] = True
env = NormalizedBoxEnv(
Reacher2D3DofBulletEnv(**env_params),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
print("Environment loaded!!")
if args.gpu:
set_gpu_mode(True)
policy.cuda()
if isinstance(policy, MakeDeterministic):
if isinstance(policy.stochastic_policy, PyTorchModule):
policy.stochastic_policy.train(False)
else:
if isinstance(policy, PyTorchModule):
policy.train(False)
while True:
if args.record:
rollout_start_fcn = lambda: \
env.start_recording_video('reacher_video.mp4')
rollout_end_fcn = lambda: \
env.stop_recording_video()
else:
rollout_start_fcn = None
rollout_end_fcn = None
obs_normalizer = data.get('obs_normalizer')
path = rollout(
env,
policy,
max_path_length=args.H,
animated=True,
obs_normalizer=obs_normalizer,
rollout_start_fcn=rollout_start_fcn,
rollout_end_fcn=rollout_end_fcn,
)
if hasattr(env, "log_diagnostics"):
env.log_diagnostics([path])
logger.dump_tabular()
if args.record:
break
def simulate_policy(args):
np.random.seed(SEED)
ptu.seed(SEED)
data = joblib.load(args.file)
if args.deterministic:
if args.un > -1:
print('Using the deterministic version of the UNintentional policy '
'%02d.' % args.un)
if 'u_policy' in data:
policy = MakeDeterministic(
MultiPolicySelector(data['u_policy'], args.un))
# WeightedMultiPolicySelector(data['u_policy'], args.un))
else:
# policy = MakeDeterministic(data['u_policies'][args.un])
if isinstance(data['policy'], TanhGaussianPolicy):
policy = MakeDeterministic(data['policy'])
else:
policy = MakeDeterministic(
WeightedMultiPolicySelector(data['policy'], args.un)
)
else:
print('Using the deterministic version of the Intentional policy.')
if isinstance(data['policy'], ExplorationPolicy):
policy = MakeDeterministic(data['policy'])
else:
policy = data['policy']
else:
if args.un > -1:
print('Using the UNintentional stochastic policy %02d' % args.un)
if 'u_policy' in data:
# policy = MultiPolicySelector(data['u_policy'], args.un)
policy = WeightedMultiPolicySelector(data['u_policy'], args.un)
else:
policy = WeightedMultiPolicySelector(data['policy'], args.un)
# policy = data['policy'][args.un]
else:
print('Using the Intentional stochastic policy.')
# policy = data['exploration_policy']
policy = data['policy']
print("Policy loaded!!")
# Load environment
dirname = os.path.dirname(args.file)
with open(os.path.join(dirname, 'variant.json')) as json_data:
log_data = json.load(json_data)
env_params = log_data['env_params']
H = int(log_data['path_length'])
env_params.pop('goal', None)
env_params['is_render'] = True
if args.subtask and args.un != -1:
env_params['subtask'] = args.un
env = NormalizedBoxEnv(
Pusher2D3DofGoalCompoEnv(**env_params),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
print("Environment loaded!!")
if args.gpu:
set_gpu_mode(True)
policy.cuda()
if isinstance(policy, MakeDeterministic):
if isinstance(policy.stochastic_policy, PyTorchModule):
policy.stochastic_policy.train(False)
else:
if isinstance(policy, PyTorchModule):
policy.train(False)
while True:
if args.record:
rollout_start_fcn = lambda: \
env.start_recording_video('pusher_video.mp4')
rollout_end_fcn = lambda: \
env.stop_recording_video()
else:
rollout_start_fcn = None
rollout_end_fcn = None
obs_normalizer = data.get('obs_normalizer')
if args.H != -1:
H = args.H
path = rollout(
env,
policy,
max_path_length=H,
animated=True,
obs_normalizer=obs_normalizer,
rollout_start_fcn=rollout_start_fcn,
rollout_end_fcn=rollout_end_fcn,
)
# plot_rollout_reward(path)
if hasattr(env, "log_diagnostics"):
env.log_diagnostics([path])
logger.dump_tabular()
if args.record:
break
def run_experiment_here(
experiment_function,
variant=None,
exp_id=0,
seed=0,
use_gpu=True,
# Logger params:
exp_prefix="default",
snapshot_mode='last',
snapshot_gap=1,
git_info=None,
script_name=None,
base_log_dir=None,
log_dir=None,
):
"""
Run an experiment locally without any serialization.
:param experiment_function: Function. `variant` will be passed in as its
only argument.
:param exp_prefix: Experiment prefix for the save file.
:param variant: Dictionary passed in to `experiment_function`.
:param exp_id: Experiment ID. Should be unique across all
experiments. Note that one experiment may correspond to multiple seeds,.
:param seed: Seed used for this experiment.
:param use_gpu: Run with GPU. By default False.
:param script_name: Name of the running script
:param log_dir: If set, set the log directory to this. Otherwise,
the directory will be auto-generated based on the exp_prefix.
:return:
"""
if variant is None:
variant = {}
variant['exp_id'] = str(exp_id)
if seed is None and 'seed' not in variant:
seed = random.randint(0, 100000)
variant['seed'] = str(seed)
reset_execution_environment()
actual_log_dir = setup_logger(
exp_prefix=exp_prefix,
variant=variant,
exp_id=exp_id,
seed=seed,
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
base_log_dir=base_log_dir,
log_dir=log_dir,
git_info=git_info,
script_name=script_name,
)
set_seed(seed)
set_gpu_mode(use_gpu)
run_experiment_here_kwargs = dict(
variant=variant,
exp_id=exp_id,
seed=seed,
use_gpu=use_gpu,
exp_prefix=exp_prefix,
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
git_info=git_info,
script_name=script_name,
base_log_dir=base_log_dir,
)
save_experiment_data(
dict(run_experiment_here_kwargs=run_experiment_here_kwargs),
actual_log_dir)
return experiment_function(variant)
