def GetTrainer(self):
"""Creates an IRLtrainer instance."""
policy = self._agent.generator
irl = self._agent.discriminator
args = self._get_args_from_params()
trainer = IRLTrainer(policy=policy,
env=self._environment,
args=args,
irl=irl,
expert_obs=self._expert_trajs["obses"],
expert_next_obs=self._expert_trajs["next_obses"],
expert_act=self._expert_trajs["acts"])
return trainer
import gym
from tf2rl.algos.ddpg import DDPG
from tf2rl.algos.vail import VAIL
from tf2rl.experiments.irl_trainer import IRLTrainer
from tf2rl.experiments.utils import restore_latest_n_traj
if __name__ == '__main__':
parser = IRLTrainer.get_argument()
parser = VAIL.get_argument(parser)
parser.add_argument('--env-name', type=str, default="RoboschoolReacher-v1")
args = parser.parse_args()
if args.expert_path_dir is None:
print("Plaese generate demonstrations first")
print("python examples/run_sac.py --env-name=RoboschoolReacher-v1 --save-test-path --test-interval=50000")
exit()
units = [400, 300]
env = gym.make(args.env_name)
test_env = gym.make(args.env_name)
policy = DDPG(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
max_action=env.action_space.high[0],
gpu=args.gpu,
actor_units=units,
critic_units=units,
n_warmup=10000,