baselines/deepq/models.py
def cnn_to_mlp(convs, hiddens, dueling=False, layer_norm=False):
    """This model takes as input an observation and returns values of all actions.

    Parameters
    ----------
    convs: [(int, int, int)]
        list of convolutional layers in form of
        (num_outputs, kernel_size, stride)
    hiddens: [int]
        list of sizes of hidden layers
    dueling: bool
        if true double the output MLP to compute a baseline
        for action scores
    layer_norm: bool
        if true applies layer normalization for every layer
        as described in https://arxiv.org/abs/1607.06450

    Returns
    -------
    q_func: function
        q_function for DQN algorithm.
    """

    return lambda *args, **kwargs: _cnn_to_mlp(convs, hiddens, dueling, layer_norm=layer_norm, *args, **kwargs)
def cnn_to_mlp(convs, hiddens, dueling=False, layer_norm=False):
    """This model takes as input an observation and returns values of all actions.

    Parameters
    ----------
    convs: [(int, int, int)]
        list of convolutional layers in form of
        (num_outputs, kernel_size, stride)
    hiddens: [int]
        list of sizes of hidden layers
    dueling: bool
        if true double the output MLP to compute a baseline
        for action scores
    layer_norm: bool
        if true applies layer normalization for every layer
        as described in https://arxiv.org/abs/1607.06450

    Returns
    -------
    q_func: function
        q_function for DQN algorithm.
    """

    return lambda *args, **kwargs: _cnn_to_mlp(convs, hiddens, dueling, layer_norm=layer_norm, *args, **kwargs)
def cnn_to_mlp ( convs , hiddens , dueling = False , layer_norm = False ) : return lambda * args , * * kwargs : _cnn_to_mlp ( convs , hiddens , dueling , layer_norm = layer_norm , * args , * * kwargs )
This model takes as input an observation and returns values of all actions.

    Parameters
    ----------
    convs: [(int, int, int)]
        list of convolutional layers in form of
        (num_outputs, kernel_size, stride)
    hiddens: [int]
        list of sizes of hidden layers
    dueling: bool
        if true double the output MLP to compute a baseline
        for action scores
    layer_norm: bool
        if true applies layer normalization for every layer
        as described in https://arxiv.org/abs/1607.06450

    Returns
    -------
    q_func: function
        q_function for DQN algorithm.
This model takes as input an observation and returns values of all actions .