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_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)
from tf2rl.algos.dqn import DQN
from tf2rl.experiments.trainer import Trainer
from tf2rl.envs.utils import make
if __name__ == '__main__':
parser = Trainer.get_argument()
parser = DQN.get_argument(parser)
parser.set_defaults(test_interval=2000)
parser.set_defaults(max_steps=100000)
parser.set_defaults(gpu=-1)
parser.set_defaults(n_warmup=500)
parser.set_defaults(batch_size=32)
parser.set_defaults(memory_capacity=int(1e4))
parser.add_argument('--env-name', type=str, default="CartPole-v0")
args = parser.parse_args()
env = make(args.env_name)
test_env = make(args.env_name)
policy = DQN(enable_double_dqn=args.enable_double_dqn,
enable_dueling_dqn=args.enable_dueling_dqn,
enable_noisy_dqn=args.enable_noisy_dqn,
state_shape=env.observation_space.shape,
action_dim=env.action_space.n,
target_replace_interval=300,
discount=0.99,
gpu=args.gpu,
memory_capacity=args.memory_capacity,
batch_size=args.batch_size,
n_warmup=args.n_warmup)
trainer = Trainer(policy, env, args, test_env=test_env)
if args.evaluate:
