def train(env_id, num_timesteps, seed): """ Train PPO1 model for Robotics environment, for testing purposes :param env_id: (str) Environment ID :param num_timesteps: (int) The total number of samples :param seed: (int) The initial seed for training """ rank = MPI.COMM_WORLD.Get_rank() with mujoco_py.ignore_mujoco_warnings(): workerseed = seed + 10000 * rank set_global_seeds(workerseed) env = make_robotics_env(env_id, workerseed, rank=rank) def policy_fn(name, ob_space, ac_space, sess=None, placeholders=None): return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, hid_size=256, num_hid_layers=3, sess=sess, placeholders=placeholders) pposgd_simple.learn(env, policy_fn, max_timesteps=num_timesteps, timesteps_per_actorbatch=2048, clip_param=0.2, entcoeff=0.0, optim_epochs=5, optim_stepsize=3e-4, optim_batchsize=256, gamma=0.99, lam=0.95, schedule='linear') env.close()
def train(env_id, num_timesteps, seed): from baselines.ppo1 import mlp_policy, pposgd_simple import baselines.common.tf_util as U rank = MPI.COMM_WORLD.Get_rank() sess = U.single_threaded_session() sess.__enter__() mujoco_py.ignore_mujoco_warnings().__enter__() workerseed = seed + 10000 * rank set_global_seeds(workerseed) env = make_robotics_env(env_id, workerseed, rank=rank) def policy_fn(name, ob_space, ac_space): return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, hid_size=256, num_hid_layers=3) pposgd_simple.learn( env, policy_fn, max_timesteps=num_timesteps, timesteps_per_actorbatch=2048, clip_param=0.2, entcoeff=0.0, optim_epochs=5, optim_stepsize=3e-4, optim_batchsize=256, gamma=0.99, lam=0.95, schedule='linear', ) env.close()
def train(env_id, num_timesteps, seed, hid_size=64, num_hid_layers=2): from baselines.ppo1 import mlp_policy, pposgd_simple assert env_id in (_MujocoEnvs + _RoboticsEnvs) def policy_fn(name, ob_space, ac_space): return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, hid_size=hid_size, num_hid_layers=num_hid_layers) if env_id in _MujocoEnvs: env = make_mujoco_env(env_id, seed) elif env_id in _RoboticsEnvs: env = make_robotics_env(env_id, seed) else: raise ValueError('Environment `{0}` is not supported.'.format(env_id)) # Not putting these params in config as we do not plan on changing them. optim_epochs = 10 if env_id in _MujocoEnvs else 5 optim_batchsize = 64 if env_id in _MujocoEnvs else 256 pi = pposgd_simple.learn(env, policy_fn, max_timesteps=num_timesteps, timesteps_per_actorbatch=2048, clip_param=0.2, entcoeff=0.0, optim_epochs=optim_epochs, optim_stepsize=3e-4, optim_batchsize=optim_batchsize, gamma=0.99, lam=0.95, schedule='linear', ) env.close() return pi