baselines/a2c/a2c.py
def learn(
    network,
    env,
    seed=None,
    nsteps=5,
    total_timesteps=int(80e6),
    vf_coef=0.5,
    ent_coef=0.01,
    max_grad_norm=0.5,
    lr=7e-4,
    lrschedule='linear',
    epsilon=1e-5,
    alpha=0.99,
    gamma=0.99,
    log_interval=100,
    load_path=None,
    **network_kwargs):

    '''
    Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.

    Parameters:
    -----------

    network:            policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
                        specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
                        tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
                        neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
                        See baselines.common/policies.py/lstm for more details on using recurrent nets in policies


    env:                RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)


    seed:               seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)

    nsteps:             int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
                        nenv is number of environment copies simulated in parallel)

    total_timesteps:    int, total number of timesteps to train on (default: 80M)

    vf_coef:            float, coefficient in front of value function loss in the total loss function (default: 0.5)

    ent_coef:           float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01)

    max_gradient_norm:  float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)

    lr:                 float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)

    lrschedule:         schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
                        returns fraction of the learning rate (specified as lr) as output

    epsilon:            float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)

    alpha:              float, RMSProp decay parameter (default: 0.99)

    gamma:              float, reward discounting parameter (default: 0.99)

    log_interval:       int, specifies how frequently the logs are printed out (default: 100)

    **network_kwargs:   keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
                        For instance, 'mlp' network architecture has arguments num_hidden and num_layers.

    '''



    set_global_seeds(seed)

    # Get the nb of env
    nenvs = env.num_envs
    policy = build_policy(env, network, **network_kwargs)

    # Instantiate the model object (that creates step_model and train_model)
    model = Model(policy=policy, env=env, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
        max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule)
    if load_path is not None:
        model.load(load_path)

    # Instantiate the runner object
    runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
    epinfobuf = deque(maxlen=100)

    # Calculate the batch_size
    nbatch = nenvs*nsteps

    # Start total timer
    tstart = time.time()

    for update in range(1, total_timesteps//nbatch+1):
        # Get mini batch of experiences
        obs, states, rewards, masks, actions, values, epinfos = runner.run()
        epinfobuf.extend(epinfos)

        policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
        nseconds = time.time()-tstart

        # Calculate the fps (frame per second)
        fps = int((update*nbatch)/nseconds)
        if update % log_interval == 0 or update == 1:
            # Calculates if value function is a good predicator of the returns (ev > 1)
            # or if it's just worse than predicting nothing (ev =< 0)
            ev = explained_variance(values, rewards)
            logger.record_tabular("nupdates", update)
            logger.record_tabular("total_timesteps", update*nbatch)
            logger.record_tabular("fps", fps)
            logger.record_tabular("policy_entropy", float(policy_entropy))
            logger.record_tabular("value_loss", float(value_loss))
            logger.record_tabular("explained_variance", float(ev))
            logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf]))
            logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf]))
            logger.dump_tabular()
    return model
def learn(
    network,
    env,
    seed=None,
    nsteps=5,
    total_timesteps=int(80e6),
    vf_coef=0.5,
    ent_coef=0.01,
    max_grad_norm=0.5,
    lr=7e-4,
    lrschedule='linear',
    epsilon=1e-5,
    alpha=0.99,
    gamma=0.99,
    log_interval=100,
    load_path=None,
    **network_kwargs):

    '''
    Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.

    Parameters:
    -----------

    network:            policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
                        specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
                        tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
                        neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
                        See baselines.common/policies.py/lstm for more details on using recurrent nets in policies


    env:                RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)


    seed:               seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)

    nsteps:             int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
                        nenv is number of environment copies simulated in parallel)

    total_timesteps:    int, total number of timesteps to train on (default: 80M)

    vf_coef:            float, coefficient in front of value function loss in the total loss function (default: 0.5)

    ent_coef:           float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01)

    max_gradient_norm:  float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)

    lr:                 float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)

    lrschedule:         schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
                        returns fraction of the learning rate (specified as lr) as output

    epsilon:            float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)

    alpha:              float, RMSProp decay parameter (default: 0.99)

    gamma:              float, reward discounting parameter (default: 0.99)

    log_interval:       int, specifies how frequently the logs are printed out (default: 100)

    **network_kwargs:   keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
                        For instance, 'mlp' network architecture has arguments num_hidden and num_layers.

    '''



    set_global_seeds(seed)

    # Get the nb of env
    nenvs = env.num_envs
    policy = build_policy(env, network, **network_kwargs)

    # Instantiate the model object (that creates step_model and train_model)
    model = Model(policy=policy, env=env, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
        max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule)
    if load_path is not None:
        model.load(load_path)

    # Instantiate the runner object
    runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
    epinfobuf = deque(maxlen=100)

    # Calculate the batch_size
    nbatch = nenvs*nsteps

    # Start total timer
    tstart = time.time()

    for update in range(1, total_timesteps//nbatch+1):
        # Get mini batch of experiences
        obs, states, rewards, masks, actions, values, epinfos = runner.run()
        epinfobuf.extend(epinfos)

        policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
        nseconds = time.time()-tstart

        # Calculate the fps (frame per second)
        fps = int((update*nbatch)/nseconds)
        if update % log_interval == 0 or update == 1:
            # Calculates if value function is a good predicator of the returns (ev > 1)
            # or if it's just worse than predicting nothing (ev =< 0)
            ev = explained_variance(values, rewards)
            logger.record_tabular("nupdates", update)
            logger.record_tabular("total_timesteps", update*nbatch)
            logger.record_tabular("fps", fps)
            logger.record_tabular("policy_entropy", float(policy_entropy))
            logger.record_tabular("value_loss", float(value_loss))
            logger.record_tabular("explained_variance", float(ev))
            logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf]))
            logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf]))
            logger.dump_tabular()
    return model
def learn ( network , env , seed = None , nsteps = 5 , total_timesteps = int ( 80e6 ) , vf_coef = 0.5 , ent_coef = 0.01 , max_grad_norm = 0.5 , lr = 7e-4 , lrschedule = 'linear' , epsilon = 1e-5 , alpha = 0.99 , gamma = 0.99 , log_interval = 100 , load_path = None , * * network_kwargs ) : set_global_seeds ( seed ) # Get the nb of env nenvs = env . num_envs policy = build_policy ( env , network , * * network_kwargs ) # Instantiate the model object (that creates step_model and train_model) model = Model ( policy = policy , env = env , nsteps = nsteps , ent_coef = ent_coef , vf_coef = vf_coef , max_grad_norm = max_grad_norm , lr = lr , alpha = alpha , epsilon = epsilon , total_timesteps = total_timesteps , lrschedule = lrschedule ) if load_path is not None : model . load ( load_path ) # Instantiate the runner object runner = Runner ( env , model , nsteps = nsteps , gamma = gamma ) epinfobuf = deque ( maxlen = 100 ) # Calculate the batch_size nbatch = nenvs * nsteps # Start total timer tstart = time . time ( ) for update in range ( 1 , total_timesteps // nbatch + 1 ) : # Get mini batch of experiences obs , states , rewards , masks , actions , values , epinfos = runner . run ( ) epinfobuf . extend ( epinfos ) policy_loss , value_loss , policy_entropy = model . train ( obs , states , rewards , masks , actions , values ) nseconds = time . time ( ) - tstart # Calculate the fps (frame per second) fps = int ( ( update * nbatch ) / nseconds ) if update % log_interval == 0 or update == 1 : # Calculates if value function is a good predicator of the returns (ev > 1) # or if it's just worse than predicting nothing (ev =< 0) ev = explained_variance ( values , rewards ) logger . record_tabular ( "nupdates" , update ) logger . record_tabular ( "total_timesteps" , update * nbatch ) logger . record_tabular ( "fps" , fps ) logger . record_tabular ( "policy_entropy" , float ( policy_entropy ) ) logger . record_tabular ( "value_loss" , float ( value_loss ) ) logger . record_tabular ( "explained_variance" , float ( ev ) ) logger . record_tabular ( "eprewmean" , safemean ( [ epinfo [ 'r' ] for epinfo in epinfobuf ] ) ) logger . record_tabular ( "eplenmean" , safemean ( [ epinfo [ 'l' ] for epinfo in epinfobuf ] ) ) logger . dump_tabular ( ) return model
Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.

    Parameters:
    -----------

    network:            policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
                        specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
                        tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
                        neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
                        See baselines.common/policies.py/lstm for more details on using recurrent nets in policies


    env:                RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)


    seed:               seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)

    nsteps:             int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
                        nenv is number of environment copies simulated in parallel)

    total_timesteps:    int, total number of timesteps to train on (default: 80M)

    vf_coef:            float, coefficient in front of value function loss in the total loss function (default: 0.5)

    ent_coef:           float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01)

    max_gradient_norm:  float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)

    lr:                 float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)

    lrschedule:         schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
                        returns fraction of the learning rate (specified as lr) as output

    epsilon:            float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)

    alpha:              float, RMSProp decay parameter (default: 0.99)

    gamma:              float, reward discounting parameter (default: 0.99)

    log_interval:       int, specifies how frequently the logs are printed out (default: 100)

    **network_kwargs:   keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
                        For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
Main entrypoint for A2C algorithm . Train a policy with given network architecture on a given environment using a2c algorithm .