| |
|
|
| from baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser |
| from baselines.common import tf_util as U |
| from baselines import logger |
|
|
| def train(env_id, num_timesteps, seed): |
| from baselines.ppo1 import mlp_policy, pposgd_simple |
| U.make_session(num_cpu=1).__enter__() |
| def policy_fn(name, ob_space, ac_space): |
| return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, |
| hid_size=64, num_hid_layers=2) |
| env = make_mujoco_env(env_id, seed) |
| pposgd_simple.learn(env, policy_fn, |
| max_timesteps=num_timesteps, |
| timesteps_per_actorbatch=2048, |
| clip_param=0.2, entcoeff=0.0, |
| optim_epochs=10, optim_stepsize=3e-4, optim_batchsize=64, |
| gamma=0.99, lam=0.95, schedule='linear', |
| ) |
| env.close() |
|
|
| def main(): |
| args = mujoco_arg_parser().parse_args() |
| logger.configure() |
| train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) |
|
|
| if __name__ == '__main__': |
| main() |
|
|
