Python trpo_mpi.learn方法代码示例

# 需要导入模块: from baselines.trpo_mpi import trpo_mpi [as 别名]
# 或者: from baselines.trpo_mpi.trpo_mpi import learn [as 别名]
def train(env_id, num_timesteps, seed):
    import baselines.common.tf_util as U
    sess = U.single_threaded_session()
    sess.__enter__()

    rank = MPI.COMM_WORLD.Get_rank()
    if rank == 0:
        logger.configure()
    else:
        logger.configure(format_strs=[])
        logger.set_level(logger.DISABLED)
    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
    def policy_fn(name, ob_space, ac_space):
        return MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
            hid_size=32, num_hid_layers=2)
    env = make_mujoco_env(env_id, workerseed)
    trpo_mpi.learn(env, policy_fn, timesteps_per_batch=1024, max_kl=0.01, cg_iters=10, cg_damping=0.1,
        max_timesteps=num_timesteps, gamma=0.99, lam=0.98, vf_iters=5, vf_stepsize=1e-3)
    env.close() 

# 需要导入模块: from baselines.trpo_mpi import trpo_mpi [as 别名]
# 或者: from baselines.trpo_mpi.trpo_mpi import learn [as 别名]
def train(env_id, num_timesteps, seed):
    whoami  = mpi_fork(num_cpu)
    if whoami == "parent":
        return
    import baselines.common.tf_util as U
    logger.session().__enter__()
    sess = U.single_threaded_session()
    sess.__enter__()

    rank = MPI.COMM_WORLD.Get_rank()
    if rank != 0:
        logger.set_level(logger.DISABLED)
    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
    set_global_seeds(workerseed)
    env = gym.make(env_id)
    def policy_fn(name, ob_space, ac_space):
        return MlpPolicy(name=name, ob_space=env.observation_space, ac_space=env.action_space,
            hid_size=32, num_hid_layers=2)
    env = bench.Monitor(env, osp.join(logger.get_dir(), "%i.monitor.json" % rank))
    env.seed(workerseed)
    gym.logger.setLevel(logging.WARN)

    trpo_mpi.learn(env, policy_fn, timesteps_per_batch=1024, max_kl=0.01, cg_iters=10, cg_damping=0.1,
        max_timesteps=num_timesteps, gamma=0.99, lam=0.98, vf_iters=5, vf_stepsize=1e-3)
    env.close() 

# 需要导入模块: from baselines.trpo_mpi import trpo_mpi [as 别名]
# 或者: from baselines.trpo_mpi.trpo_mpi import learn [as 别名]
def train(env_id, num_timesteps, seed):
    import baselines.common.tf_util as U
    sess = U.single_threaded_session()
    sess.__enter__()

    rank = MPI.COMM_WORLD.Get_rank()
    if rank != 0:
        logger.set_level(logger.DISABLED)
    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
    set_global_seeds(workerseed)
    env = gym.make(env_id)
    def policy_fn(name, ob_space, ac_space):
        return MlpPolicy(name=name, ob_space=env.observation_space, ac_space=env.action_space,
            hid_size=32, num_hid_layers=2)
    env = bench.Monitor(env, logger.get_dir() and
        osp.join(logger.get_dir(), str(rank)))
    env.seed(workerseed)
    gym.logger.setLevel(logging.WARN)

    trpo_mpi.learn(env, policy_fn, timesteps_per_batch=1024, max_kl=0.01, cg_iters=10, cg_damping=0.1,
        max_timesteps=num_timesteps, gamma=0.99, lam=0.98, vf_iters=5, vf_stepsize=1e-3)
    env.close() 

# 需要导入模块: from baselines.trpo_mpi import trpo_mpi [as 别名]
# 或者: from baselines.trpo_mpi.trpo_mpi import learn [as 别名]
def train(env_id, num_timesteps, seed):
    from baselines.trpo_mpi.nosharing_cnn_policy import CnnPolicy
    from baselines.trpo_mpi import trpo_mpi
    import baselines.common.tf_util as U
    rank = MPI.COMM_WORLD.Get_rank()
    sess = U.single_threaded_session()
    sess.__enter__()
    if rank == 0:
        logger.configure()
    else:
        logger.configure(format_strs=[])

    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
    set_global_seeds(workerseed)
    env = make_atari(env_id)
    def policy_fn(name, ob_space, ac_space): #pylint: disable=W0613
        return CnnPolicy(name=name, ob_space=env.observation_space, ac_space=env.action_space)
    env = bench.Monitor(env, logger.get_dir() and osp.join(logger.get_dir(), str(rank)))
    env.seed(workerseed)

    env = wrap_deepmind(env)
    env.seed(workerseed)

    trpo_mpi.learn(env, policy_fn, timesteps_per_batch=512, max_kl=0.001, cg_iters=10, cg_damping=1e-3,
        max_timesteps=int(num_timesteps * 1.1), gamma=0.98, lam=1.0, vf_iters=3, vf_stepsize=1e-4, entcoeff=0.00)
    env.close() 

# 需要导入模块: from baselines.trpo_mpi import trpo_mpi [as 别名]
# 或者: from baselines.trpo_mpi.trpo_mpi import learn [as 别名]
def train(env_id, num_timesteps, seed, num_cpu):
    from baselines.trpo_mpi.nosharing_cnn_policy import CnnPolicy
    from baselines.trpo_mpi import trpo_mpi
    import baselines.common.tf_util as U
    whoami  = mpi_fork(num_cpu)
    if whoami == "parent":
        return
    rank = MPI.COMM_WORLD.Get_rank()
    sess = U.single_threaded_session()
    sess.__enter__()
    logger.session().__enter__()
    if rank != 0:
        logger.set_level(logger.DISABLED)


    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
    set_global_seeds(workerseed)
    env = gym.make(env_id)
    def policy_fn(name, ob_space, ac_space): #pylint: disable=W0613
        return CnnPolicy(name=name, ob_space=env.observation_space, ac_space=env.action_space)
    env = bench.Monitor(env, osp.join(logger.get_dir(), "%i.monitor.json"%rank))
    env.seed(workerseed)
    gym.logger.setLevel(logging.WARN)

    env = wrap_train(env)
    num_timesteps /= 4 # because we're wrapping the envs to do frame skip
    env.seed(workerseed)

    trpo_mpi.learn(env, policy_fn, timesteps_per_batch=512, max_kl=0.001, cg_iters=10, cg_damping=1e-3,
        max_timesteps=num_timesteps, gamma=0.98, lam=1.0, vf_iters=3, vf_stepsize=1e-4, entcoeff=0.00)
    env.close() 

# 需要导入模块: from baselines.trpo_mpi import trpo_mpi [as 别名]
# 或者: from baselines.trpo_mpi.trpo_mpi import learn [as 别名]
def train(env_id, num_timesteps, seed):
    import baselines.common.tf_util as U
    sess = U.single_threaded_session()
    sess.__enter__()
    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
    def policy_fn(name, ob_space, ac_space):
        return MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
            hid_size=32, num_hid_layers=2)

    # Create a new base directory like /tmp/openai-2018-05-21-12-27-22-552435
    log_dir = os.path.join(energyplus_logbase_dir(), datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"))
    if not os.path.exists(log_dir + '/output'):
        os.makedirs(log_dir + '/output')
    os.environ["ENERGYPLUS_LOG"] = log_dir
    model = os.getenv('ENERGYPLUS_MODEL')
    if model is None:
        print('Environment variable ENERGYPLUS_MODEL is not defined')
        os.exit()
    weather = os.getenv('ENERGYPLUS_WEATHER')
    if weather is None:
        print('Environment variable ENERGYPLUS_WEATHER is not defined')
        os.exit()

    rank = MPI.COMM_WORLD.Get_rank()
    if rank == 0:
        print('train: init logger with dir={}'.format(log_dir)) #XXX
        logger.configure(log_dir)
    else:
        logger.configure(format_strs=[])
        logger.set_level(logger.DISABLED)

    env = make_energyplus_env(env_id, workerseed)

    trpo_mpi.learn(env, policy_fn,
                   max_timesteps=num_timesteps,
                   #timesteps_per_batch=1*1024, max_kl=0.01, cg_iters=10, cg_damping=0.1,
                   timesteps_per_batch=16*1024, max_kl=0.01, cg_iters=10, cg_damping=0.1,
                   gamma=0.99, lam=0.98, vf_iters=5, vf_stepsize=1e-3)
    env.close() 

# 需要导入模块: from baselines.trpo_mpi import trpo_mpi [as 别名]
# 或者: from baselines.trpo_mpi.trpo_mpi import learn [as 别名]
def train(env_id, num_timesteps, seed):
    from baselines.trpo_mpi.nosharing_cnn_policy import CnnPolicy
    from baselines.trpo_mpi import trpo_mpi
    import baselines.common.tf_util as U
    rank = MPI.COMM_WORLD.Get_rank()
    sess = U.single_threaded_session()
    sess.__enter__()
    if rank == 0:
        logger.configure()
    else:
        logger.configure(format_strs=[])

    workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
    set_global_seeds(workerseed)
    env = make_atari(env_id)
    def policy_fn(name, ob_space, ac_space): #pylint: disable=W0613
        return CnnPolicy(name=name, ob_space=env.observation_space, ac_space=env.action_space)
    env = bench.Monitor(env, logger.get_dir() and osp.join(logger.get_dir(), str(rank)))
    env.seed(workerseed)
    gym.logger.setLevel(logging.WARN)

    env = wrap_deepmind(env)
    env.seed(workerseed)

    trpo_mpi.learn(env, policy_fn, timesteps_per_batch=512, max_kl=0.001, cg_iters=10, cg_damping=1e-3,
        max_timesteps=int(num_timesteps * 1.1), gamma=0.98, lam=1.0, vf_iters=3, vf_stepsize=1e-4, entcoeff=0.00)
    env.close() 

# 需要导入模块: from baselines.trpo_mpi import trpo_mpi [as 别名]
# 或者: from baselines.trpo_mpi.trpo_mpi import learn [as 别名]
def main():
    # use fixed random state
    rand_state = np.random.RandomState(1).get_state()
    np.random.set_state(rand_state)
    tf_set_seeds(np.random.randint(1, 2**31 - 1))

    # Create the Create2 docker environment
    env = Create2DockerEnv(30, port='/dev/ttyUSB0', ir_window=20, ir_history=1,
                           obs_history=1, dt=0.045, random_state=rand_state)
    env = NormalizedEnv(env)

    # Start environment processes
    env.start()

    # Create baselines TRPO policy function
    sess = U.single_threaded_session()
    sess.__enter__()
    def policy_fn(name, ob_space, ac_space):
        return MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
            hid_size=32, num_hid_layers=2)

    # Create and start plotting process
    plot_running = Value('i', 1)
    shared_returns = Manager().dict({"write_lock": False,
                                     "episodic_returns": [],
                                     "episodic_lengths": [], })
    # Spawn plotting process
    pp = Process(target=plot_create2_docker, args=(env, 2048, shared_returns, plot_running))
    pp.start()

    # Create callback function for logging data from baselines TRPO learn
    kindred_callback = create_callback(shared_returns)

    # Train baselines TRPO
    learn(env, policy_fn,
          max_timesteps=40000,
          timesteps_per_batch=2048,
          max_kl=0.05,
          cg_iters=10,
          cg_damping=0.1,
          vf_iters=5,
          vf_stepsize=0.001,
          gamma=0.995,
          lam=0.995,
          callback=kindred_callback
          )

    # Safely terminate plotter process
    plot_running.value = 0  # shutdown ploting process
    time.sleep(2)
    pp.join()

    env.close() 

# 需要导入模块: from baselines.trpo_mpi import trpo_mpi [as 别名]
# 或者: from baselines.trpo_mpi.trpo_mpi import learn [as 别名]
def main():
    # use fixed random state
    rand_state = np.random.RandomState(1).get_state()
    np.random.set_state(rand_state)
    tf_set_seeds(np.random.randint(1, 2**31 - 1))

    # Create the Create2 mover environment
    env = Create2MoverEnv(90, port='/dev/ttyUSB0', obs_history=1, dt=0.15, random_state=rand_state)
    env = NormalizedEnv(env)

    # Start environment processes
    env.start()

    # Create baselines TRPO policy function
    sess = U.single_threaded_session()
    sess.__enter__()
    def policy_fn(name, ob_space, ac_space):
        return MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
            hid_size=32, num_hid_layers=2)

    # Create and start plotting process
    plot_running = Value('i', 1)
    shared_returns = Manager().dict({"write_lock": False,
                                     "episodic_returns": [],
                                     "episodic_lengths": [], })
    # Spawn plotting process
    pp = Process(target=plot_create2_mover, args=(env, 2048, shared_returns, plot_running))
    pp.start()

    # Create callback function for logging data from baselines TRPO learn
    kindred_callback = create_callback(shared_returns)

    # Train baselines TRPO
    learn(env, policy_fn,
          max_timesteps=40000,
          timesteps_per_batch=2048,
          max_kl=0.05,
          cg_iters=10,
          cg_damping=0.1,
          vf_iters=5,
          vf_stepsize=0.001,
          gamma=0.995,
          lam=0.995,
          callback=kindred_callback
          )

    # Safely terminate plotter process
    plot_running.value = 0  # shutdown ploting process
    time.sleep(2)
    pp.join()

    env.close()