def main(args):
dataset, env = get_pybullet(args.dataset)
d3rlpy.seed(args.seed)
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)
device = None if args.gpu is None else Device(args.gpu)
encoder_factory = VectorEncoderFactory(hidden_units=[256, 256, 256, 256])
awac = AWAC(actor_encoder_factory=encoder_factory,
critic_encoder_factory=encoder_factory,
q_func_factory=args.q_func,
use_gpu=device)
awac.fit(train_episodes,
eval_episodes=test_episodes,
n_epochs=1000,
scorers={
'environment': evaluate_on_environment(env),
'td_error': td_error_scorer,
'discounted_advantage': discounted_sum_of_advantage_scorer,
'value_scale': average_value_estimation_scorer,
'value_std': value_estimation_std_scorer,
'action_diff': continuous_action_diff_scorer
})
def main(args):
dataset, env = get_pybullet(args.dataset)
d3rlpy.seed(args.seed)
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)
device = None if args.gpu is None else Device(args.gpu)
dynamics = ProbabilisticEnsembleDynamics(use_gpu=device)
dynamics.fit(train_episodes,
eval_episodes=test_episodes,
n_steps=100000,
scorers={
"obs_error": dynamics_observation_prediction_error_scorer,
"reward_error": dynamics_reward_prediction_error_scorer,
})
combo = COMBO(q_func_factory=args.q_func,
dynamics=dynamics,
use_gpu=device)
combo.fit(train_episodes,
eval_episodes=test_episodes,
n_steps=1000000,
scorers={
'environment': evaluate_on_environment(env),
'td_error': td_error_scorer,
'discounted_advantage': discounted_sum_of_advantage_scorer,
'value_scale': average_value_estimation_scorer,
'value_std': value_estimation_std_scorer,
'action_diff': continuous_action_diff_scorer
})
def main(args):
dataset, env = get_pybullet(args.dataset)
d3rlpy.seed(args.seed)
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)
device = None if args.gpu is None else Device(args.gpu)
bc = BC(n_epochs=100, use_gpu=device)
bc.fit(train_episodes,
eval_episodes=test_episodes,
scorers={
'environment': evaluate_on_environment(env),
'action_diff': continuous_action_diff_scorer
})
def main(args):
dataset, env = get_pybullet(args.dataset)
d3rlpy.seed(args.seed)
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)
device = None if args.gpu is None else Device(args.gpu)
awr = AWR(n_epochs=100, use_gpu=device)
awr.fit(train_episodes,
eval_episodes=test_episodes,
scorers={
'environment': evaluate_on_environment(env),
'td_error': td_error_scorer,
'value_scale': average_value_estimation_scorer,
'action_diff': continuous_action_diff_scorer
})
def main(args):
dataset, env = get_pybullet(args.dataset)
d3rlpy.seed(args.seed)
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)
device = None if args.gpu is None else Device(args.gpu)
sac = SAC(n_epochs=100, q_func_type=args.q_func_type, use_gpu=device)
sac.fit(train_episodes,
eval_episodes=test_episodes,
scorers={
'environment': evaluate_on_environment(env),
'td_error': td_error_scorer,
'discounted_advantage': discounted_sum_of_advantage_scorer,
'value_scale': average_value_estimation_scorer,
'value_std': value_estimation_std_scorer,
'action_diff': continuous_action_diff_scorer
})
from sklearn.model_selection import train_test_split
from d3rlpy.datasets import get_pybullet
from d3rlpy.algos import CQL
from d3rlpy.ope import FQE
from d3rlpy.metrics.scorer import evaluate_on_environment
from d3rlpy.metrics.scorer import initial_state_value_estimation_scorer
from d3rlpy.metrics.scorer import soft_opc_scorer
dataset, env = get_pybullet('hopper-bullet-mixed-v0')
train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)
# train algorithm
cql = CQL(n_epochs=100, use_gpu=True)
cql.fit(train_episodes,
eval_episodes=test_episodes,
scorers={
'environment': evaluate_on_environment(env),
'init_value': initial_state_value_estimation_scorer,
'soft_opc': soft_opc_scorer(600)
})
# or load the trained model
# cql = CQL.from_json('<path-to-json>/params.json')
# cql.load_model('<path-to-model>/model.pt')
# evaluate the trained policy
fqe = FQE(algo=cql,
n_epochs=200,
q_func_factory='qr',
learning_rate=1e-4,
from d3rlpy.algos import AWAC
from d3rlpy.datasets import get_pybullet
from d3rlpy.online.buffers import ReplayBuffer
from d3rlpy.metrics.scorer import evaluate_on_environment
from d3rlpy.metrics.scorer import discounted_sum_of_advantage_scorer
from d3rlpy.metrics.scorer import average_value_estimation_scorer
from sklearn.model_selection import train_test_split
# prepare dataset and environment
dataset, env = get_pybullet('hopper-bullet-random-v0')
_, eval_env = get_pybullet('hopper-bullet-random-v0')
train_episodes, test_episodes = train_test_split(dataset)
# setup algorithm
awac = AWAC(encoder_params={'hidden_units': [256, 256, 256, 256]},
use_gpu=True)
## pretrain
awac.fit(train_episodes[:10000],
eval_episodes=test_episodes,
n_epochs=30,
scorers={
'environment': evaluate_on_environment(env),
'advantage': discounted_sum_of_advantage_scorer,
'value_scale': average_value_estimation_scorer
})
# fine-tuning
awac.fit_online(env,
ReplayBuffer(1000000, env, train_episodes[:10000]),
