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_discrete(self):
from tf2rl.algos.dqn import DQN
parser = DQN.get_argument(self.parser)
parser.set_defaults(n_warmup=1)
args, _ = parser.parse_known_args()
def env_fn():
return gym.make("CartPole-v0")
def policy_fn(env, name, memory_capacity=int(1e6), gpu=-1, *args, **kwargs):
return DQN(
name=name,
state_shape=env.observation_space.shape,
action_dim=env.action_space.n,
n_warmup=500,
target_replace_interval=300,
batch_size=32,
memory_capacity=memory_capacity,
discount=0.99,
gpu=-1)
def get_weights_fn(policy):
return [policy.q_func.weights,
policy.q_func_target.weights]
def set_weights_fn(policy, weights):
q_func_weights, qfunc_target_weights = weights
update_target_variables(
policy.q_func.weights, q_func_weights, tau=1.)
update_target_variables(
policy.q_func_target.weights, qfunc_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 test_run_discrete(self):
from tf2rl.algos.dqn import DQN
parser = DQN.get_argument(self.parser)
parser.set_defaults(n_warmup=1)
args, _ = parser.parse_known_args()
run(args, env_fn_discrete, policy_fn_discrete, get_weights_fn_discrete,
set_weights_fn_discrete)
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)
action_dim=env.action_space.high.size,
max_action=env.action_space.high[0],
gpu=gpu,
name=name,
sigma=noise_level,
batch_size=100,
lr_actor=0.001,
lr_critic=0.001,
actor_units=[400, 300],
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.)
run(args, env_fn, policy_fn, get_weights_fn, set_weights_fn)
parser = DQN.get_argument(parser)
parser.add_argument('--atari', action='store_true')
parser.add_argument('--env-name',
type=str,
default="SpaceInvadersNoFrameskip-v4")
args = parser.parse_args()
if args.atari:
env_name = args.env_name
n_warmup = 50000
target_replace_interval = 10000
batch_size = 32
optimizer = tf.keras.optimizers.Adam(learning_rate=0.0000625,
epsilon=1.5e-4)
epsilon_decay_rate = int(1e6)
QFunc = AtariQFunc
else:
env_name = "CartPole-v0"
n_warmup = 500
target_replace_interval = 300
batch_size = 32
optimizer = None
epsilon_decay_rate = int(1e3)
QFunc = None
run(
args, env_fn(env_name),
policy_fn(args, n_warmup, target_replace_interval, batch_size,
optimizer, epsilon_decay_rate, QFunc), get_weights_fn,
set_weights_fn)
