Python utils.batch_to_seq方法代码示例

# 需要导入模块: from baselines.a2c import utils [as 别名]
# 或者: from baselines.a2c.utils import batch_to_seq [as 别名]
def cnn_lstm(nlstm=128, layer_norm=False, **conv_kwargs):
    def network_fn(X, nenv=1):
        nbatch = X.shape[0] 
        nsteps = nbatch // nenv
         
        h = nature_cnn(X, **conv_kwargs)
       
        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
        S = tf.placeholder(tf.float32, [nenv, 2*nlstm]) #states

        xs = batch_to_seq(h, nenv, nsteps)
        ms = batch_to_seq(M, nenv, nsteps)

        if layer_norm:
            h5, snew = utils.lnlstm(xs, ms, S, scope='lnlstm', nh=nlstm)
        else:
            h5, snew = utils.lstm(xs, ms, S, scope='lstm', nh=nlstm)
            
        h = seq_to_batch(h5)
        initial_state = np.zeros(S.shape.as_list(), dtype=float)

        return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}

    return network_fn 

# 需要导入模块: from baselines.a2c import utils [as 别名]
# 或者: from baselines.a2c.utils import batch_to_seq [as 别名]
def cnn_lstm(nlstm=128, layer_norm=False, **conv_kwargs):
    def network_fn(X, nenv=1):
        nbatch = X.shape[0]
        nsteps = nbatch // nenv

        h = nature_cnn(X, **conv_kwargs)

        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
        S = tf.placeholder(tf.float32, [nenv, 2*nlstm]) #states

        xs = batch_to_seq(h, nenv, nsteps)
        ms = batch_to_seq(M, nenv, nsteps)

        if layer_norm:
            h5, snew = utils.lnlstm(xs, ms, S, scope='lnlstm', nh=nlstm)
        else:
            h5, snew = utils.lstm(xs, ms, S, scope='lstm', nh=nlstm)

        h = seq_to_batch(h5)
        initial_state = np.zeros(S.shape.as_list(), dtype=float)

        return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}

    return network_fn 

# 需要导入模块: from baselines.a2c import utils [as 别名]
# 或者: from baselines.a2c.utils import batch_to_seq [as 别名]
def cnn_lstm(nlstm=128, layer_norm=False, conv_fn=nature_cnn, **conv_kwargs):
    def network_fn(X, nenv=1):
        nbatch = X.shape[0]
        nsteps = nbatch // nenv

        h = conv_fn(X, **conv_kwargs)

        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
        S = tf.placeholder(tf.float32, [nenv, 2*nlstm]) #states

        xs = batch_to_seq(h, nenv, nsteps)
        ms = batch_to_seq(M, nenv, nsteps)

        if layer_norm:
            h5, snew = utils.lnlstm(xs, ms, S, scope='lnlstm', nh=nlstm)
        else:
            h5, snew = utils.lstm(xs, ms, S, scope='lstm', nh=nlstm)

        h = seq_to_batch(h5)
        initial_state = np.zeros(S.shape.as_list(), dtype=float)

        return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}

    return network_fn 

# 需要导入模块: from baselines.a2c import utils [as 别名]
# 或者: from baselines.a2c.utils import batch_to_seq [as 别名]
def __init__(self, sess, ob_space, ac_space, nbatch, nsteps, nlstm=256, reuse=False):
        nenv = nbatch // nsteps
        nh, nw, nc = ob_space.shape
        ob_shape = (nbatch, nh, nw, nc)
        nact = ac_space.n
        X = tf.placeholder(tf.uint8, ob_shape) #obs
        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
        S = tf.placeholder(tf.float32, [nenv, nlstm*2]) #states
        with tf.variable_scope("model", reuse=reuse):
            h = nature_cnn(X)
            xs = batch_to_seq(h, nenv, nsteps)
            ms = batch_to_seq(M, nenv, nsteps)
            h5, snew = lnlstm(xs, ms, S, 'lstm1', nh=nlstm)
            h5 = seq_to_batch(h5)
            pi = fc(h5, 'pi', nact)
            vf = fc(h5, 'v', 1)

        self.pdtype = make_pdtype(ac_space)
        self.pd = self.pdtype.pdfromflat(pi)

        v0 = vf[:, 0]
        a0 = self.pd.sample()
        neglogp0 = self.pd.neglogp(a0)
        self.initial_state = np.zeros((nenv, nlstm*2), dtype=np.float32)

        def step(ob, state, mask):
            return sess.run([a0, v0, snew, neglogp0], {X:ob, S:state, M:mask})

        def value(ob, state, mask):
            return sess.run(v0, {X:ob, S:state, M:mask})

        self.X = X
        self.M = M
        self.S = S
        self.pi = pi
        self.vf = vf
        self.step = step
        self.value = value 

# 需要导入模块: from baselines.a2c import utils [as 别名]
# 或者: from baselines.a2c.utils import batch_to_seq [as 别名]
def __init__(self, sess, ob_space, ac_space, nenv, nsteps, nstack, reuse=False, nlstm=256):
        nbatch = nenv * nsteps
        nh, nw, nc = ob_space.shape
        ob_shape = (nbatch, nh, nw, nc * nstack)
        nact = ac_space.n
        X = tf.placeholder(tf.uint8, ob_shape)  # obs
        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
        S = tf.placeholder(tf.float32, [nenv, nlstm*2]) #states
        with tf.variable_scope("model", reuse=reuse):
            h = nature_cnn(X)

            # lstm
            xs = batch_to_seq(h, nenv, nsteps)
            ms = batch_to_seq(M, nenv, nsteps)
            h5, snew = lstm(xs, ms, S, 'lstm1', nh=nlstm)
            h5 = seq_to_batch(h5)

            pi_logits = fc(h5, 'pi', nact, init_scale=0.01)
            pi = tf.nn.softmax(pi_logits)
            q = fc(h5, 'q', nact)

        a = sample(pi_logits)  # could change this to use self.pi instead
        self.initial_state = np.zeros((nenv, nlstm*2), dtype=np.float32)
        self.X = X
        self.M = M
        self.S = S
        self.pi = pi  # actual policy params now
        self.q = q

        def step(ob, state, mask, *args, **kwargs):
            # returns actions, mus, states
            a0, pi0, s = sess.run([a, pi, snew], {X: ob, S: state, M: mask})
            return a0, pi0, s

        self.step = step 

# 需要导入模块: from baselines.a2c import utils [as 别名]
# 或者: from baselines.a2c.utils import batch_to_seq [as 别名]
def q_retrace(R, D, q_i, v, rho_i, nenvs, nsteps, gamma):
    """
    Calculates q_retrace targets

    :param R: Rewards
    :param D: Dones
    :param q_i: Q values for actions taken
    :param v: V values
    :param rho_i: Importance weight for each action
    :return: Q_retrace values
    """
    rho_bar = batch_to_seq(tf.minimum(1.0, rho_i), nenvs, nsteps, True)  # list of len steps, shape [nenvs]
    rs = batch_to_seq(R, nenvs, nsteps, True)  # list of len steps, shape [nenvs]
    ds = batch_to_seq(D, nenvs, nsteps, True)  # list of len steps, shape [nenvs]
    q_is = batch_to_seq(q_i, nenvs, nsteps, True)
    vs = batch_to_seq(v, nenvs, nsteps + 1, True)
    v_final = vs[-1]
    qret = v_final
    qrets = []
    for i in range(nsteps - 1, -1, -1):
        check_shape([qret, ds[i], rs[i], rho_bar[i], q_is[i], vs[i]], [[nenvs]] * 6)
        qret = rs[i] + gamma * qret * (1.0 - ds[i])
        qrets.append(qret)
        qret = (rho_bar[i] * (qret - q_is[i])) + vs[i]
    qrets = qrets[::-1]
    qret = seq_to_batch(qrets, flat=True)
    return qret

# For ACER with PPO clipping instead of trust region
# def clip(ratio, eps_clip):
#     # assume 0 <= eps_clip <= 1
#     return tf.minimum(1 + eps_clip, tf.maximum(1 - eps_clip, ratio)) 

# 需要导入模块: from baselines.a2c import utils [as 别名]
# 或者: from baselines.a2c.utils import batch_to_seq [as 别名]
def strip(var, nenvs, nsteps, flat = False):
    vars = batch_to_seq(var, nenvs, nsteps + 1, flat)
    return seq_to_batch(vars[:-1], flat)