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VRBPO_test.py
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VRBPO_test.py
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import torch
import garage
# from garage.experiment import run_experiment
from garage.experiment import LocalRunner
# from garage.tf.envs import TfEnv
from Policy import GaussianMLPPolicy, CategoricalMLPPolicy
from Algorithms.VR_BPO import VR_BGPO
from gym.envs.mujoco import Walker2dEnv, HopperEnv,HalfCheetahEnv
# from gym.envs.classic_control import CartPoleEnv
from garage import wrap_experiment
from garage.envs import GarageEnv, normalize
from garage.envs.normalized_env import NormalizedEnv
from garage.torch.value_functions import GaussianMLPValueFunction
# garage.envs.normalized_env
import os.path
from os import path
from Algorithms._utils import CosLR
import argparse
parser = argparse.ArgumentParser(description='VR_BPO')
parser.add_argument('--env', default='CartPole', type=str, help='choose environment from [CartPole, Walker, Hopper, HalfCheetah]')
parser.add_argument('--type', default='Diag', type=str)
parser.add_argument('--pow', default=2.0, type=float)
parser.add_argument('--n_counts', default=5, type=int)
args = parser.parse_args()
@wrap_experiment
def run_task(ctxt=None, *_):
"""Set up environment and algorithm and run the task.
Args:
snapshot_config (garage.experiment.SnapshotConfig): The snapshot
configuration used by LocalRunner to create the snapshotter.
If None, it will create one with default settings.
_ : Unused parameters
"""
#count = 1
g_max = 0.05
lam = 0.1
grad_factor = 0.001
n_epochs = 100
vf_lr = 2.5e-4
n_counts = args.n_counts
vf_minibatch = 128
minibatch_size = 128
th=1.2
loss_clip=False
sch = None
runner = LocalRunner(ctxt)
print(args.env)
entropy_method='max'
stop_entropy_gradient=True
if args.env == 'CartPole':
#CartPole
gymenv = GarageEnv(env_name='CartPole-v1')
env = gymenv
batch_size = 5000
max_length = 100
minibatch_size = 64
vf_minibatch = 64
name = 'CartPole'
lr = 0.75
c = 100
w = 1
lam = 0.0015
discount = 0.995
g_max = 1.0
model_path = './init/CartPole_policy.pth'
elif args.env == 'Acrobot':
env = GarageEnv(env_name='Acrobot-v1')
batch_size = 50000
max_length = 500
minibatch_size = 256
vf_minibatch = 256
name = 'Acrobot'
lr = 0.0175
c = 12000*4
w = 1
g_max = 1.0
lam = 0.00125
discount = 0.99
model_path = './init/Acrobot_policy.pth'
elif args.env == 'MountainCar':
env = GarageEnv(env_name='MountainCarContinuous-v0')
batch_size = 50000
max_length = 500
minibatch_size = 256
vf_minibatch = 256
name = 'MountainCar'
lr = 0.0175
c = 6000*2
w = 1
grad_factor = 0.00002
g_max = 1.0
# lam = 1e-3
lam = 2.5e-4
discount = 0.99
n_epochs = 150
model_path = './init/MountainCar_policy.pth'
elif args.env == 'DPendulum':
gymenv = GarageEnv(env_name='InvertedDoublePendulum-v2')
env = gymenv
batch_size = 50000
max_length = 500
minibatch_size = 256
vf_minibatch = 256
name = 'DPendulm'
# grad_factor = 100
th = 1.2
# batchsize:50
lr = 0.75
c = 40
w = 1
lam = 0.02
g_max = 0.3
discount = 0.99
loss_clip=True
model_path = './init/Pendulum_policy.pth'
elif args.env == 'Pendulum':
env = GarageEnv(env_name='InvertedPendulum-v2')
# gymenv = GarageEnv(env_name='InvertedDoublePendulum-v2')
# env = gymenv
batch_size = 50000
max_length = 500
# n_timestep = 5e5
# n_counts = 5
minibatch_size = 256
vf_minibatch = 256
name = 'Pendulm'
# grad_factor = 100
th = 1.2
# batchsize:50
lr = 0.75
c = 40
w = 1
lam = 0.02
g_max = 0.3
loss_clip = True
discount = 0.99
model_path = './init/Single_Pendulum_policy.pth'
elif args.env == 'Acrobot':
env = GarageEnv(env_name='Acrobot-v1')
batch_size = 50000
max_length = 500
minibatch_size = 512
vf_minibatch = 512
name = 'Acrobot'
lr = 0.0175
c = 12000
w = 1
grad_factor = 0.00002
if args.type == 'Diag':
g_max = 1.0
lam = 0.001
else:
g_max = 0.05
lam = 0.001
discount = 0.99
model_path = './init/Acrobot_policy.pth'
elif args.env == 'MountainCar':
env = GarageEnv(env_name='MountainCarContinuous-v0')
batch_size = 50000
max_length = 500
minibatch_size = 512
vf_minibatch = 512
name = 'MountainCar'
lr = 0.0175
c = 6000
w = 1
discount = 0.99
grad_factor = 0.00002
if args.type == 'Diag':
lr = 0.0175
g_max = 1.0
c = 12000
lam = 1e-3
grad_factor = 0.00002
else:
g_max = 0.05
lam = 0.001
if args.pow == 3.0:
lam = 4e-4
n_epochs = 150
model_path = './init/MountainCar_policy.pth'
# Swimmer - v2
elif args.env == 'Swim':
# Reacher - v2
env = GarageEnv(env_name='Swimmer-v2')
env = NormalizedEnv(env, normalize_obs=True, normalize_reward=False,)
batch_size = 50000
max_length = 500
n_epochs = 200
# n_timestep = 5e5
# n_counts = 5
minibatch_size = 512
vf_minibatch = 512
# grad_factor = 100
th = 1.2
vf_lr=2e-4
# batchsize:50
lr = 0.5
c = 40
w = 1
lam = 7.5e-3
sch = CosLR(lam, T_max=n_epochs)
g_max = 1.0
discount = 0.99
loss_clip = True
entropy_method = 'no_entropy'
stop_entropy_gradient = False
model_path = './init/Swim_Policy.pth'
name = 'Swim'
elif args.env == 'Reacher':
# Reacher - v2
env = GarageEnv(env_name='Reacher-v2')
# env = NormalizedEnv(env, normalize_obs=True, normalize_reward=False, )
batch_size = 50000
max_length = 500
n_epochs = 200
minibatch_size = 512
vf_minibatch = 512
th = 1.2
lr = 0.75
c = 25
w = 1
lam = 5e-4
g_max = 1.0
discount = 0.99
loss_clip = True
entropy_method = 'no_entropy'
stop_entropy_gradient = False
model_path = './init/Reacher_Policy.pth'
name = 'Reacher'
elif args.env == 'Walker':
env = GarageEnv(env_name='Walker2d-v2')
batch_size = 50000
max_length = 500
minibatch_size = 512
vf_minibatch = 512
n_epochs = 200
th = 1.2
lr = 0.75
c = 25
w = 1
lam = 0.0025
g_max = 1.0
discount = 0.99
loss_clip = True
# entropy_method = 'regularized'
entropy_method = 'no_entropy'
stop_entropy_gradient = False
model_path = './init/Walker_Policy.pth'
elif args.env == 'HalfCheetah':
env = GarageEnv(env_name='HalfCheetah-v2')
batch_size = 50000
max_length = 500
minibatch_size = 512
n_epochs = 200
lr = 0.75
c = 25
w = 1
# lam = 0.001
lam = 5e-4
g_max = 1.0
discount = 0.99
loss_clip = True
model_path = './init/HalfCheetah_Policy.pth'
name = 'HalfCheetah'
log_dir = './log/VRBPO-%s_%s_%d_%d.txt' % (args.type, args.env, batch_size, max_length)
for i in range(n_counts):
if args.env == 'CartPole':
policy = CategoricalMLPPolicy(env.spec,
hidden_sizes=[8, 8],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
elif args.env == 'Acrobot':
policy = CategoricalMLPPolicy(env.spec,
hidden_sizes=[8, 8],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
else:
policy = GaussianMLPPolicy(env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
if path.exists(model_path):
policy.load_state_dict(torch.load(model_path))
else:
torch.save(policy.state_dict(), model_path)
algo = VR_BGPO(env_spec=env.spec,
policy=policy,
value_function=value_function,
max_path_length=max_length,
dist_type=args.type,
dist_pow=args.pow,
vf_minibatch_size=vf_minibatch,
minibatch_size=minibatch_size,
discount=discount,
grad_factor=grad_factor,
lam=lam,
policy_lr=lr,
vf_lr=vf_lr,
c=c,
w=w,
loss_clip=loss_clip,
th=th,
sch=sch,
center_adv=False,
g_max=g_max,
entropy_method=entropy_method,
stop_entropy_gradient=stop_entropy_gradient,
log_dir=log_dir
)
runner.setup(algo, env)
runner.train(n_epochs=n_epochs, batch_size=batch_size)
run_task()