def test_run_continuous(self):
from tf2rl.algos.ddpg import DDPG
parser = DDPG.get_argument(self.parser)
parser.set_defaults(n_warmup=1)
args, _ = parser.parse_known_args()
def env_fn():
return gym.make('Pendulum-v0')
def policy_fn(env, name, memory_capacity=int(1e6), gpu=-1, *args, **kwargs):
return DDPG(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
n_warmup=500,
gpu=-1)
def get_weights_fn(policy):
return [policy.actor.weights,
policy.critic.weights,
policy.critic_target.weights]
def set_weights_fn(policy, weights):
actor_weights, critic_weights, critic_target_weights = weights
update_target_variables(
policy.actor.weights, actor_weights, tau=1.)
update_target_variables(
policy.critic.weights, critic_weights, tau=1.)
update_target_variables(
policy.critic_target.weights, critic_target_weights, tau=1.)
run(args, env_fn, policy_fn, get_weights_fn, set_weights_fn)
def _test_run_continuous(parser):
from tf2rl.algos.ddpg import DDPG
parser = DDPG.get_argument(parser)
args = parser.parse_args()
def env_fn():
return gym.make('Pendulum-v0')
sample_env = env_fn()
def policy_fn(env, name, memory_capacity=int(1e6), gpu=-1):
return DDPG(state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=-1)
def get_weights_fn(policy):
return [
policy.actor.weights, policy.critic.weights,
policy.critic_target.weights
]
def set_weights_fn(policy, weights):
actor_weights, critic_weights, critic_target_weights = weights
update_target_variables(policy.actor.weights, actor_weights, tau=1.)
update_target_variables(policy.critic.weights, critic_weights, tau=1.)
update_target_variables(policy.critic_target.weights,
critic_target_weights,
tau=1.)
run(args, env_fn, policy_fn, get_weights_fn, set_weights_fn)
def test_run_continuous(self):
from tf2rl.algos.ddpg import DDPG
parser = DDPG.get_argument(self.parser)
parser.set_defaults(n_warmup=1)
args, _ = parser.parse_known_args()
run(args, env_fn_continuous, policy_fn_continuous,
get_weights_fn_continuous, set_weights_fn_continuous)
def get_argument(parser=None):
"""
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
"""
parser = DDPG.get_argument(parser)
parser.add_argument('--eta', type=float, default=0.05)
return parser
critic_units=[400, 300],
memory_capacity=memory_capacity)
def get_weights_fn(policy):
# TODO: Check if following needed
import tensorflow as tf
with tf.device(policy.device):
return [
policy.actor.weights, policy.critic.weights,
policy.critic_target.weights
]
def set_weights_fn(policy, weights):
actor_weights, critic_weights, critic_target_weights = weights
update_target_variables(policy.actor.weights, actor_weights, tau=1.)
update_target_variables(policy.critic.weights, critic_weights, tau=1.)
update_target_variables(policy.critic_target.weights,
critic_target_weights,
tau=1.)
if __name__ == '__main__':
parser = apex_argument()
parser.add_argument('--env-name', type=str, default="Pendulum-v0")
parser = DDPG.get_argument(parser)
args = parser.parse_args()
run(args, env_fn(args.env_name), policy_fn, get_weights_fn, set_weights_fn)
def get_argument(parser=None):
parser = DDPG.get_argument(parser)
parser.add_argument('--eta', type=float, default=0.05)
return parser