baselines/common/input.py
def encode_observation(ob_space, placeholder):
    '''
    Encode input in the way that is appropriate to the observation space

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

    ob_space: gym.Space             observation space

    placeholder: tf.placeholder     observation input placeholder
    '''
    if isinstance(ob_space, Discrete):
        return tf.to_float(tf.one_hot(placeholder, ob_space.n))
    elif isinstance(ob_space, Box):
        return tf.to_float(placeholder)
    elif isinstance(ob_space, MultiDiscrete):
        placeholder = tf.cast(placeholder, tf.int32)
        one_hots = [tf.to_float(tf.one_hot(placeholder[..., i], ob_space.nvec[i])) for i in range(placeholder.shape[-1])]
        return tf.concat(one_hots, axis=-1)
    else:
        raise NotImplementedError
def encode_observation(ob_space, placeholder):
    '''
    Encode input in the way that is appropriate to the observation space

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

    ob_space: gym.Space             observation space

    placeholder: tf.placeholder     observation input placeholder
    '''
    if isinstance(ob_space, Discrete):
        return tf.to_float(tf.one_hot(placeholder, ob_space.n))
    elif isinstance(ob_space, Box):
        return tf.to_float(placeholder)
    elif isinstance(ob_space, MultiDiscrete):
        placeholder = tf.cast(placeholder, tf.int32)
        one_hots = [tf.to_float(tf.one_hot(placeholder[..., i], ob_space.nvec[i])) for i in range(placeholder.shape[-1])]
        return tf.concat(one_hots, axis=-1)
    else:
        raise NotImplementedError
def encode_observation ( ob_space , placeholder ) : if isinstance ( ob_space , Discrete ) : return tf . to_float ( tf . one_hot ( placeholder , ob_space . n ) ) elif isinstance ( ob_space , Box ) : return tf . to_float ( placeholder ) elif isinstance ( ob_space , MultiDiscrete ) : placeholder = tf . cast ( placeholder , tf . int32 ) one_hots = [ tf . to_float ( tf . one_hot ( placeholder [ ... , i ] , ob_space . nvec [ i ] ) ) for i in range ( placeholder . shape [ - 1 ] ) ] return tf . concat ( one_hots , axis = - 1 ) else : raise NotImplementedError
Encode input in the way that is appropriate to the observation space

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

    ob_space: gym.Space             observation space

    placeholder: tf.placeholder     observation input placeholder
Encode input in the way that is appropriate to the observation space