import gym
from tf2rl.algos.vpg import VPG
from tf2rl.experiments.on_policy_trainer import OnPolicyTrainer
from tf2rl.envs.utils import is_discrete, get_act_dim
if __name__ == '__main__':
parser = OnPolicyTrainer.get_argument()
parser = VPG.get_argument(parser)
parser.add_argument('--env-name', type=str,
default="Pendulum-v0")
parser.set_defaults(test_interval=10240)
parser.set_defaults(max_steps=int(1e7))
parser.set_defaults(horizon=512)
parser.set_defaults(batch_size=32)
parser.set_defaults(gpu=-1)
args = parser.parse_args()
env = gym.make(args.env_name)
test_env = gym.make(args.env_name)
policy = VPG(
state_shape=env.observation_space.shape,
action_dim=get_act_dim(env.action_space),
is_discrete=is_discrete(env.action_space),
max_action=None if is_discrete(
env.action_space) else env.action_space.high[0],
batch_size=args.batch_size,
actor_units=[32, 32],
critic_units=[32, 32],
discount=0.9,
def setUpClass(cls):
cls.agent = VPG(state_shape=cls.continuous_env.observation_space.shape,
action_dim=cls.continuous_env.action_space.low.size,
is_discrete=False,
gpu=-1)
def setUpClass(cls):
cls.agent = VPG(state_shape=cls.discrete_env.observation_space.shape,
action_dim=cls.discrete_env.action_space.n,
is_discrete=True,
gpu=-1)
from tf2rl.algos.vpg import VPG
from tf2rl.experiments.on_policy_trainer import OnPolicyTrainer
from tf2rl.envs.utils import is_discrete, get_act_dim, make
if __name__ == '__main__':
parser = OnPolicyTrainer.get_argument()
parser = VPG.get_argument(parser)
parser.add_argument('--env-name', type=str, default="Pendulum-v0")
parser.set_defaults(test_interval=10240)
parser.set_defaults(max_steps=int(1e7))
parser.set_defaults(horizon=512)
parser.set_defaults(batch_size=32)
parser.set_defaults(gpu=-1)
args = parser.parse_args()
env = make(args.env_name)
test_env = make(args.env_name)
policy = VPG(state_shape=env.observation_space.shape,
action_dim=get_act_dim(env.action_space),
is_discrete=is_discrete(env.action_space),
max_action=None if is_discrete(env.action_space) else
env.action_space.high[0],
batch_size=args.batch_size,
actor_units=(64, 64),
critic_units=(64, 64),
n_epoch=10,
lr_actor=3e-4,
lr_critic=3e-4,
hidden_activation_actor="tanh",
hidden_activation_critic="tanh",
discount=0.9,