def _build_net(self, reuse=None, custom_getter=None, trainable=None): """ Set up q network based on class attributes. This function uses layers defined in rllab.tf. Args: reuse: A bool indicates whether reuse variables in the same scope. custom_getter: A customized getter object used to get variables. trainable: A bool indicates whether variables are trainable. """ with tf.variable_scope(self.name, reuse=reuse, custom_getter=custom_getter): l_obs = L.InputLayer(shape=(None, self._obs_dim), name="obs") l_action = L.InputLayer(shape=(None, self._action_dim), name="actions") n_layers = len(self._hidden_sizes) + 1 if n_layers > 1: action_merge_layer = \ (self._action_merge_layer % n_layers + n_layers) % n_layers else: action_merge_layer = 1 l_hidden = l_obs for idx, size in enumerate(self._hidden_sizes): if self._batch_norm: l_hidden = batch_norm(l_hidden) if idx == action_merge_layer: l_hidden = L.ConcatLayer([l_hidden, l_action]) l_hidden = L.DenseLayer(l_hidden, num_units=size, nonlinearity=self._hidden_nonlinearity, trainable=trainable, name="hidden_%d" % (idx + 1)) if action_merge_layer == n_layers: l_hidden = L.ConcatLayer([l_hidden, l_action]) l_output = L.DenseLayer(l_hidden, num_units=1, nonlinearity=self._output_nonlinearity, trainable=trainable, name="output") output_var = L.get_output(l_output) self._f_qval = tensor_utils.compile_function( [l_obs.input_var, l_action.input_var], output_var) self._output_layer = l_output self._obs_layer = l_obs self._action_layer = l_action LayersPowered.__init__(self, [l_output])
def build_net(self, trainable=True, name=None): """ Set up q network based on class attributes. This function uses layers defined in garage.tf. Args: reuse: A bool indicates whether reuse variables in the same scope. trainable: A bool indicates whether variables are trainable. """ with tf.variable_scope(name): l_obs = L.InputLayer(shape=(None, self._obs_dim), name="obs") l_action = L.InputLayer(shape=(None, self._action_dim), name="actions") n_layers = len(self._hidden_sizes) + 1 if n_layers > 1: action_merge_layer = \ (self._action_merge_layer % n_layers + n_layers) % n_layers else: action_merge_layer = 1 l_hidden = l_obs for idx, size in enumerate(self._hidden_sizes): if self._batch_norm: l_hidden = batch_norm(l_hidden) if idx == action_merge_layer: l_hidden = L.ConcatLayer([l_hidden, l_action]) l_hidden = L.DenseLayer(l_hidden, num_units=size, nonlinearity=self._hidden_nonlinearity, trainable=trainable, name="hidden_%d" % (idx + 1)) if action_merge_layer == n_layers: l_hidden = L.ConcatLayer([l_hidden, l_action]) l_output = L.DenseLayer(l_hidden, num_units=1, nonlinearity=self._output_nonlinearity, trainable=trainable, name="output") output_var = L.get_output(l_output) f_qval = tensor_utils.compile_function( [l_obs.input_var, l_action.input_var], output_var) output_layer = l_output obs_layer = l_obs action_layer = l_action return f_qval, output_layer, obs_layer, action_layer
def __init__( self, output_dim, hidden_sizes, hidden_nonlinearity, output_nonlinearity, name=None, hidden_w_init=ly.XavierUniformInitializer(), hidden_b_init=tf.zeros_initializer(), output_w_init=ly.XavierUniformInitializer(), output_b_init=tf.zeros_initializer(), input_var=None, input_layer=None, input_shape=None, batch_normalization=False, weight_normalization=False, ): Serializable.quick_init(self, locals()) with tf.variable_scope(name, "MLP"): if input_layer is None: l_in = ly.InputLayer( shape=(None, ) + input_shape, input_var=input_var, name="input") else: l_in = input_layer self._layers = [l_in] l_hid = l_in if batch_normalization: l_hid = ly.batch_norm(l_hid) for idx, hidden_size in enumerate(hidden_sizes): l_hid = ly.DenseLayer( l_hid, num_units=hidden_size, nonlinearity=hidden_nonlinearity, name="hidden_%d" % idx, w=hidden_w_init, b=hidden_b_init, weight_normalization=weight_normalization) if batch_normalization: l_hid = ly.batch_norm(l_hid) self._layers.append(l_hid) l_out = ly.DenseLayer( l_hid, num_units=output_dim, nonlinearity=output_nonlinearity, name="output", w=output_w_init, b=output_b_init, weight_normalization=weight_normalization) if batch_normalization: l_out = ly.batch_norm(l_out) self._layers.append(l_out) self._l_in = l_in self._l_out = l_out LayersPowered.__init__(self, l_out)
def _build_net(self, reuse=None, custom_getter=None, trainable=None): """ Set up q network based on class attributes. This function uses layers defined in garage.tf. Args: reuse: A bool indicates whether reuse variables in the same scope. custom_getter: A customized getter object used to get variables. trainable: A bool indicates whether variables are trainable. """ with tf.variable_scope(self.name, reuse=reuse, custom_getter=custom_getter): l_in = layers.InputLayer(shape=(None, self._obs_dim), name="obs") l_hidden = l_in for idx, hidden_size in enumerate(self._hidden_sizes): if self._batch_norm: l_hidden = batch_norm(l_hidden) l_hidden = layers.DenseLayer( l_hidden, hidden_size, nonlinearity=self._hidden_nonlinearity, trainable=trainable, name="hidden_%d" % idx) l_output = layers.DenseLayer( l_hidden, self._action_dim, nonlinearity=self._output_nonlinearity, trainable=trainable, name="output") with tf.name_scope(self._policy_network_name): action = layers.get_output(l_output) scaled_action = tf.multiply(action, self._action_bound, name="scaled_action") self._f_prob_online = tensor_utils.compile_function( inputs=[l_in.input_var], outputs=scaled_action) self._output_layer = l_output self._obs_layer = l_in LayersPowered.__init__(self, [l_output])
def build_net(self, trainable=True, name=None): """ Set up q network based on class attributes. This function uses layers defined in garage.tf. Args: reuse: A bool indicates whether reuse variables in the same scope. trainable: A bool indicates whether variables are trainable. """ with tf.variable_scope(name): l_in = layers.InputLayer(shape=(None, self._obs_dim), name='obs') l_hidden = l_in for idx, hidden_size in enumerate(self._hidden_sizes): if self._batch_norm: l_hidden = batch_norm(l_hidden) l_hidden = layers.DenseLayer( l_hidden, hidden_size, nonlinearity=self._hidden_nonlinearity, trainable=trainable, name='hidden_%d' % idx) l_output = layers.DenseLayer( l_hidden, self._action_dim, nonlinearity=self._output_nonlinearity, trainable=trainable, name='output') with tf.name_scope(self._policy_network_name): action = layers.get_output(l_output) scaled_action = tf.multiply(action, self._action_bound, name='scaled_action') f_prob_online = tensor_utils.compile_function(inputs=[l_in.input_var], outputs=scaled_action) output_layer = l_output obs_layer = l_in return f_prob_online, output_layer, obs_layer
def __init__( self, env_spec, name="GaussianGRUPolicy", hidden_dim=32, feature_network=None, state_include_action=True, hidden_nonlinearity=tf.tanh, gru_layer_cls=L.GRULayer, learn_std=True, init_std=1.0, output_nonlinearity=None, std_share_network=False, ): """ :param env_spec: A spec for the env. :param hidden_dim: dimension of hidden layer :param hidden_nonlinearity: nonlinearity used for each hidden layer :return: """ assert isinstance(env_spec.action_space, Box) self._mean_network_name = "mean_network" self._std_network_name = "std_network" with tf.variable_scope(name, "GaussianGRUPolicy"): Serializable.quick_init(self, locals()) super(GaussianGRUPolicy, self).__init__(env_spec) obs_dim = env_spec.observation_space.flat_dim action_dim = env_spec.action_space.flat_dim if state_include_action: input_dim = obs_dim + action_dim else: input_dim = obs_dim l_input = L.InputLayer(shape=(None, None, input_dim), name="input") if feature_network is None: feature_dim = input_dim l_flat_feature = None l_feature = l_input else: feature_dim = feature_network.output_layer.output_shape[-1] l_flat_feature = feature_network.output_layer l_feature = L.OpLayer( l_flat_feature, extras=[l_input], name="reshape_feature", op=lambda flat_feature, input: tf.reshape( flat_feature, tf.stack([ tf.shape(input)[0], tf.shape(input)[1], feature_dim ])), shape_op=lambda _, input_shape: (input_shape[0], input_shape[1], feature_dim)) if std_share_network: mean_network = GRUNetwork( input_shape=(feature_dim, ), input_layer=l_feature, output_dim=2 * action_dim, hidden_dim=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=output_nonlinearity, gru_layer_cls=gru_layer_cls, name="gru_mean_network") l_mean = L.SliceLayer(mean_network.output_layer, slice(action_dim), name="mean_slice") l_step_mean = L.SliceLayer(mean_network.step_output_layer, slice(action_dim), name="step_mean_slice") l_log_std = L.SliceLayer(mean_network.output_layer, slice(action_dim, 2 * action_dim), name="log_std_slice") l_step_log_std = L.SliceLayer(mean_network.step_output_layer, slice(action_dim, 2 * action_dim), name="step_log_std_slice") else: mean_network = GRUNetwork( input_shape=(feature_dim, ), input_layer=l_feature, output_dim=action_dim, hidden_dim=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=output_nonlinearity, gru_layer_cls=gru_layer_cls, name="gru_mean_network") l_mean = mean_network.output_layer l_step_mean = mean_network.step_output_layer l_log_std = L.ParamLayer( mean_network.input_layer, num_units=action_dim, param=tf.constant_initializer(np.log(init_std)), name="output_log_std", trainable=learn_std, ) l_step_log_std = L.ParamLayer( mean_network.step_input_layer, num_units=action_dim, param=l_log_std.param, name="step_output_log_std", trainable=learn_std, ) self.mean_network = mean_network self.feature_network = feature_network self.l_input = l_input self.state_include_action = state_include_action flat_input_var = tf.placeholder(dtype=tf.float32, shape=(None, input_dim), name="flat_input") if feature_network is None: feature_var = flat_input_var else: feature_var = L.get_output( l_flat_feature, {feature_network.input_layer: flat_input_var}) with tf.name_scope(self._mean_network_name): out_step_mean, out_step_hidden_mean = L.get_output( [l_step_mean, mean_network.step_hidden_layer], {mean_network.step_input_layer: feature_var}) out_step_mean = tf.identity(out_step_mean, "step_mean") out_step_hidden_mean = tf.identity(out_step_hidden_mean, "step_hidden_mean") with tf.name_scope(self._std_network_name): out_step_log_std = L.get_output( l_step_log_std, {mean_network.step_input_layer: feature_var}) out_step_log_std = tf.identity(out_step_log_std, "step_log_std") self.f_step_mean_std = tensor_utils.compile_function([ flat_input_var, mean_network.step_prev_state_layer.input_var, ], [out_step_mean, out_step_log_std, out_step_hidden_mean]) self.l_mean = l_mean self.l_log_std = l_log_std self.input_dim = input_dim self.action_dim = action_dim self.hidden_dim = hidden_dim self.prev_actions = None self.prev_hiddens = None self.dist = RecurrentDiagonalGaussian(action_dim) self.name = name out_layers = [l_mean, l_log_std, l_step_log_std] if feature_network is not None: out_layers.append(feature_network.output_layer) LayersPowered.__init__(self, out_layers)
def __init__(self, input_shape, output_dim, conv_filters, conv_filter_sizes, conv_strides, conv_pads, hidden_sizes, hidden_nonlinearity, output_nonlinearity, name=None, hidden_w_init=ly.XavierUniformInitializer(), hidden_b_init=tf.zeros_initializer(), output_w_init=ly.XavierUniformInitializer(), output_b_init=tf.zeros_initializer(), input_var=None, input_layer=None, batch_normalization=False, weight_normalization=False): Serializable.quick_init(self, locals()) """ A network composed of several convolution layers followed by some fc layers. input_shape: (width,height,channel) HOWEVER, network inputs are assumed flattened. This network will first unflatten the inputs and then apply the standard convolutions and so on. conv_filters: a list of numbers of convolution kernel conv_filter_sizes: a list of sizes (int) of the convolution kernels conv_strides: a list of strides (int) of the conv kernels conv_pads: a list of pad formats (either 'SAME' or 'VALID') hidden_nonlinearity: a nonlinearity from tf.nn, shared by all conv and fc layers hidden_sizes: a list of numbers of hidden units for all fc layers """ with tf.variable_scope(name, 'ConvNetwork'): if input_layer is not None: l_in = input_layer l_hid = l_in elif len(input_shape) == 3: l_in = ly.InputLayer(shape=(None, np.prod(input_shape)), input_var=input_var, name='input') l_hid = ly.reshape(l_in, ([0], ) + input_shape, name='reshape_input') elif len(input_shape) == 2: l_in = ly.InputLayer(shape=(None, np.prod(input_shape)), input_var=input_var, name='input') input_shape = (1, ) + input_shape l_hid = ly.reshape(l_in, ([0], ) + input_shape, name='reshape_input') else: l_in = ly.InputLayer(shape=(None, ) + input_shape, input_var=input_var, name='input') l_hid = l_in if batch_normalization: l_hid = ly.batch_norm(l_hid) for idx, conv_filter, filter_size, stride, pad in zip( range(len(conv_filters)), conv_filters, conv_filter_sizes, conv_strides, conv_pads, ): l_hid = ly.Conv2DLayer( l_hid, num_filters=conv_filter, filter_size=filter_size, stride=(stride, stride), pad=pad, nonlinearity=hidden_nonlinearity, name='conv_hidden_%d' % idx, weight_normalization=weight_normalization, ) if batch_normalization: l_hid = ly.batch_norm(l_hid) if output_nonlinearity == ly.spatial_expected_softmax: assert not hidden_sizes assert output_dim == conv_filters[-1] * 2 l_hid.nonlinearity = tf.identity l_out = ly.SpatialExpectedSoftmaxLayer(l_hid) else: l_hid = ly.flatten(l_hid, name='conv_flatten') for idx, hidden_size in enumerate(hidden_sizes): l_hid = ly.DenseLayer( l_hid, num_units=hidden_size, nonlinearity=hidden_nonlinearity, name='hidden_%d' % idx, w=hidden_w_init, b=hidden_b_init, weight_normalization=weight_normalization, ) if batch_normalization: l_hid = ly.batch_norm(l_hid) l_out = ly.DenseLayer( l_hid, num_units=output_dim, nonlinearity=output_nonlinearity, name='output', w=output_w_init, b=output_b_init, weight_normalization=weight_normalization, ) if batch_normalization: l_out = ly.batch_norm(l_out) self._l_in = l_in self._l_out = l_out # self._input_var = l_in.input_var LayersPowered.__init__(self, l_out)
def __init__(self, input_shape, extra_input_shape, output_dim, hidden_sizes, conv_filters, conv_filter_sizes, conv_strides, conv_pads, name=None, extra_hidden_sizes=None, hidden_w_init=ly.XavierUniformInitializer(), hidden_b_init=tf.zeros_initializer(), output_w_init=ly.XavierUniformInitializer(), output_b_init=tf.zeros_initializer(), hidden_nonlinearity=tf.nn.relu, output_nonlinearity=None, input_var=None, input_layer=None): Serializable.quick_init(self, locals()) if extra_hidden_sizes is None: extra_hidden_sizes = [] with tf.variable_scope(name, 'ConvMergeNetwork'): input_flat_dim = np.prod(input_shape) extra_input_flat_dim = np.prod(extra_input_shape) total_input_flat_dim = input_flat_dim + extra_input_flat_dim if input_layer is None: l_in = ly.InputLayer(shape=(None, total_input_flat_dim), input_var=input_var, name='input') else: l_in = input_layer l_conv_in = ly.reshape(ly.SliceLayer(l_in, indices=slice(input_flat_dim), name='conv_slice'), ([0], ) + input_shape, name='conv_reshaped') l_extra_in = ly.reshape(ly.SliceLayer(l_in, indices=slice( input_flat_dim, None), name='extra_slice'), ([0], ) + extra_input_shape, name='extra_reshaped') l_conv_hid = l_conv_in for idx, conv_filter, filter_size, stride, pad in zip( range(len(conv_filters)), conv_filters, conv_filter_sizes, conv_strides, conv_pads, ): l_conv_hid = ly.Conv2DLayer( l_conv_hid, num_filters=conv_filter, filter_size=filter_size, stride=(stride, stride), pad=pad, nonlinearity=hidden_nonlinearity, name='conv_hidden_%d' % idx, ) l_extra_hid = l_extra_in for idx, hidden_size in enumerate(extra_hidden_sizes): l_extra_hid = ly.DenseLayer( l_extra_hid, num_units=hidden_size, nonlinearity=hidden_nonlinearity, name='extra_hidden_%d' % idx, w=hidden_w_init, b=hidden_b_init, ) l_joint_hid = ly.concat( [ly.flatten(l_conv_hid, name='conv_hidden_flat'), l_extra_hid], name='joint_hidden') for idx, hidden_size in enumerate(hidden_sizes): l_joint_hid = ly.DenseLayer( l_joint_hid, num_units=hidden_size, nonlinearity=hidden_nonlinearity, name='joint_hidden_%d' % idx, w=hidden_w_init, b=hidden_b_init, ) l_out = ly.DenseLayer( l_joint_hid, num_units=output_dim, nonlinearity=output_nonlinearity, name='output', w=output_w_init, b=output_b_init, ) self._l_in = l_in self._l_out = l_out LayersPowered.__init__(self, [l_out], input_layers=[l_in])
def __init__(self, input_shape, output_dim, hidden_dim, name=None, hidden_nonlinearity=tf.nn.relu, output_w_init=ly.XavierUniformInitializer(), recurrent_nonlinearity=tf.nn.sigmoid, recurrent_w_x_init=ly.XavierUniformInitializer(), recurrent_w_h_init=ly.OrthogonalInitializer(), lstm_layer_cls=ly.LSTMLayer, output_nonlinearity=None, input_var=None, input_layer=None, forget_bias=1.0, use_peepholes=False, layer_args=None): with tf.variable_scope(name, 'LSTMNetwork'): if input_layer is None: l_in = ly.InputLayer(shape=(None, None) + input_shape, input_var=input_var, name='input') else: l_in = input_layer l_step_input = ly.InputLayer(shape=(None, ) + input_shape, name='step_input') # contains previous hidden and cell state l_step_prev_state = ly.InputLayer(shape=(None, hidden_dim * 2), name='step_prev_state') if layer_args is None: layer_args = dict() l_lstm = lstm_layer_cls(l_in, num_units=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, gate_nonlinearity=recurrent_nonlinearity, hidden_init_trainable=False, name='lstm_layer', forget_bias=forget_bias, cell_init_trainable=False, w_x_init=recurrent_w_x_init, w_h_init=recurrent_w_h_init, use_peepholes=use_peepholes, **layer_args) l_lstm_flat = ly.ReshapeLayer(l_lstm, shape=(-1, hidden_dim), name='lstm_flat') l_output_flat = ly.DenseLayer(l_lstm_flat, num_units=output_dim, nonlinearity=output_nonlinearity, w=output_w_init, name='output_flat') l_output = ly.OpLayer( l_output_flat, op=lambda flat_output, l_input: tf.reshape( flat_output, tf.stack( (tf.shape(l_input)[0], tf.shape(l_input)[1], -1))), shape_op=lambda flat_output_shape, l_input_shape: (l_input_shape[0], l_input_shape[1], flat_output_shape[-1]), extras=[l_in], name='output') l_step_state = l_lstm.get_step_layer(l_step_input, l_step_prev_state, name='step_state') l_step_hidden = ly.SliceLayer(l_step_state, indices=slice(hidden_dim), name='step_hidden') l_step_cell = ly.SliceLayer(l_step_state, indices=slice(hidden_dim, None), name='step_cell') l_step_output = ly.DenseLayer(l_step_hidden, num_units=output_dim, nonlinearity=output_nonlinearity, w=l_output_flat.w, b=l_output_flat.b, name='step_output') self._l_in = l_in self._hid_init_param = l_lstm.h0 self._cell_init_param = l_lstm.c0 self._l_lstm = l_lstm self._l_out = l_output self._l_step_input = l_step_input self._l_step_prev_state = l_step_prev_state self._l_step_hidden = l_step_hidden self._l_step_cell = l_step_cell self._l_step_state = l_step_state self._l_step_output = l_step_output self._hidden_dim = hidden_dim
def __init__(self, input_shape, output_dim, hidden_dim, name=None, hidden_nonlinearity=tf.nn.relu, output_w_init=ly.XavierUniformInitializer(), recurrent_nonlinearity=tf.nn.sigmoid, recurrent_w_x_init=ly.XavierUniformInitializer(), recurrent_w_h_init=ly.OrthogonalInitializer(), gru_layer_cls=ly.GRULayer, output_nonlinearity=None, input_var=None, input_layer=None, layer_args=None): with tf.variable_scope(name, 'GRUNetwork'): if input_layer is None: l_in = ly.InputLayer(shape=(None, None) + input_shape, input_var=input_var, name='input') else: l_in = input_layer l_step_input = ly.InputLayer(shape=(None, ) + input_shape, name='step_input') l_step_prev_state = ly.InputLayer(shape=(None, hidden_dim), name='step_prev_state') if layer_args is None: layer_args = dict() l_gru = gru_layer_cls(l_in, num_units=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, gate_nonlinearity=recurrent_nonlinearity, hidden_init_trainable=False, w_x_init=recurrent_w_x_init, w_h_init=recurrent_w_h_init, name='gru', **layer_args) l_gru_flat = ly.ReshapeLayer(l_gru, shape=(-1, hidden_dim), name='gru_flat') l_output_flat = ly.DenseLayer(l_gru_flat, num_units=output_dim, nonlinearity=output_nonlinearity, w=output_w_init, name='output_flat') l_output = ly.OpLayer( l_output_flat, op=lambda flat_output, l_input: tf.reshape( flat_output, tf.stack( (tf.shape(l_input)[0], tf.shape(l_input)[1], -1))), shape_op=lambda flat_output_shape, l_input_shape: (l_input_shape[0], l_input_shape[1], flat_output_shape[-1]), extras=[l_in], name='output') l_step_state = l_gru.get_step_layer(l_step_input, l_step_prev_state, name='step_state') l_step_hidden = l_step_state l_step_output = ly.DenseLayer(l_step_hidden, num_units=output_dim, nonlinearity=output_nonlinearity, w=l_output_flat.w, b=l_output_flat.b, name='step_output') self._l_in = l_in self._hid_init_param = l_gru.h0 self._l_gru = l_gru self._l_out = l_output self._l_step_input = l_step_input self._l_step_prev_state = l_step_prev_state self._l_step_hidden = l_step_hidden self._l_step_state = l_step_state self._l_step_output = l_step_output self._hidden_dim = hidden_dim
def __init__( self, env_spec, name="CategoricalGRUPolicy", hidden_dim=32, feature_network=None, state_include_action=True, hidden_nonlinearity=tf.tanh, gru_layer_cls=L.GRULayer, ): """ :param env_spec: A spec for the env. :param hidden_dim: dimension of hidden layer :param hidden_nonlinearity: nonlinearity used for each hidden layer :return: """ assert isinstance(env_spec.action_space, Discrete) self._prob_network_name = "prob_network" with tf.variable_scope(name, "CategoricalGRUPolicy"): Serializable.quick_init(self, locals()) super(CategoricalGRUPolicy, self).__init__(env_spec) obs_dim = env_spec.observation_space.flat_dim action_dim = env_spec.action_space.flat_dim if state_include_action: input_dim = obs_dim + action_dim else: input_dim = obs_dim l_input = L.InputLayer(shape=(None, None, input_dim), name="input") if feature_network is None: feature_dim = input_dim l_flat_feature = None l_feature = l_input else: feature_dim = feature_network.output_layer.output_shape[-1] l_flat_feature = feature_network.output_layer l_feature = L.OpLayer( l_flat_feature, extras=[l_input], name="reshape_feature", op=lambda flat_feature, input: tf.reshape( flat_feature, tf.stack([ tf.shape(input)[0], tf.shape(input)[1], feature_dim ])), shape_op=lambda _, input_shape: (input_shape[0], input_shape[1], feature_dim)) prob_network = GRUNetwork(input_shape=(feature_dim, ), input_layer=l_feature, output_dim=env_spec.action_space.n, hidden_dim=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=tf.nn.softmax, gru_layer_cls=gru_layer_cls, name=self._prob_network_name) self.prob_network = prob_network self.feature_network = feature_network self.l_input = l_input self.state_include_action = state_include_action flat_input_var = tf.placeholder(dtype=tf.float32, shape=(None, input_dim), name="flat_input") if feature_network is None: feature_var = flat_input_var else: with tf.name_scope("feature_network", values=[flat_input_var]): feature_var = L.get_output( l_flat_feature, {feature_network.input_layer: flat_input_var}) with tf.name_scope(self._prob_network_name, values=[feature_var]): out_prob_step, out_prob_hidden = L.get_output( [ prob_network.step_output_layer, prob_network.step_hidden_layer ], {prob_network.step_input_layer: feature_var}) out_prob_step = tf.identity(out_prob_step, "prob_step_output") out_prob_hidden = tf.identity(out_prob_hidden, "prob_step_hidden") self.f_step_prob = tensor_utils.compile_function( [flat_input_var, prob_network.step_prev_state_layer.input_var], [out_prob_step, out_prob_hidden]) self.input_dim = input_dim self.action_dim = action_dim self.hidden_dim = hidden_dim self.name = name self.prev_actions = None self.prev_hiddens = None self.dist = RecurrentCategorical(env_spec.action_space.n) out_layers = [prob_network.output_layer] if feature_network is not None: out_layers.append(feature_network.output_layer) LayersPowered.__init__(self, out_layers)
def __init__(self, env_spec, name='CategoricalLSTMPolicy', hidden_dim=32, hidden_nonlinearity=tf.nn.tanh, recurrent_nonlinearity=tf.nn.sigmoid, recurrent_w_x_init=L.XavierUniformInitializer(), recurrent_w_h_init=L.OrthogonalInitializer(), output_nonlinearity=tf.nn.softmax, output_w_init=L.XavierUniformInitializer(), feature_network=None, prob_network=None, state_include_action=True, forget_bias=1.0, use_peepholes=False, lstm_layer_cls=L.LSTMLayer): """ :param env_spec: A spec for the env. :param hidden_dim: dimension of hidden layer :param hidden_nonlinearity: nonlinearity used for each hidden layer :return: """ assert isinstance(env_spec.action_space, Discrete) self._prob_network_name = 'prob_network' with tf.variable_scope(name, 'CategoricalLSTMPolicy'): Serializable.quick_init(self, locals()) super(CategoricalLSTMPolicy, self).__init__(env_spec) obs_dim = env_spec.observation_space.flat_dim action_dim = env_spec.action_space.flat_dim if state_include_action: input_dim = obs_dim + action_dim else: input_dim = obs_dim l_input = L.InputLayer(shape=(None, None, input_dim), name='input') if feature_network is None: feature_dim = input_dim l_flat_feature = None l_feature = l_input else: feature_dim = feature_network.output_layer.output_shape[-1] l_flat_feature = feature_network.output_layer l_feature = L.OpLayer( l_flat_feature, extras=[l_input], name='reshape_feature', op=lambda flat_feature, input: tf.reshape( flat_feature, tf.stack([ tf.shape(input)[0], tf.shape(input)[1], feature_dim ])), shape_op=lambda _, input_shape: (input_shape[0], input_shape[1], feature_dim)) if prob_network is None: prob_network = LSTMNetwork( input_shape=(feature_dim, ), input_layer=l_feature, output_dim=env_spec.action_space.n, hidden_dim=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, recurrent_nonlinearity=recurrent_nonlinearity, recurrent_w_x_init=recurrent_w_x_init, recurrent_w_h_init=recurrent_w_h_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, forget_bias=forget_bias, use_peepholes=use_peepholes, lstm_layer_cls=lstm_layer_cls, name=self._prob_network_name) self.prob_network = prob_network self.feature_network = feature_network self.l_input = l_input self.state_include_action = state_include_action flat_input_var = tf.placeholder(dtype=tf.float32, shape=(None, input_dim), name='flat_input') if feature_network is None: feature_var = flat_input_var else: with tf.name_scope('feature_network', values=[flat_input_var]): feature_var = L.get_output( l_flat_feature, {feature_network.input_layer: flat_input_var}) with tf.name_scope(self._prob_network_name, values=[feature_var]): out_prob_step, out_prob_hidden, out_step_cell = L.get_output( [ prob_network.step_output_layer, prob_network.step_hidden_layer, prob_network.step_cell_layer ], {prob_network.step_input_layer: feature_var}) self.f_step_prob = tensor_utils.compile_function([ flat_input_var, prob_network.step_prev_state_layer.input_var, ], [out_prob_step, out_prob_hidden, out_step_cell]) self.input_dim = input_dim self.action_dim = action_dim self.hidden_dim = hidden_dim self.name = name self.prev_actions = None self.prev_hiddens = None self.prev_cells = None self.dist = RecurrentCategorical(env_spec.action_space.n) out_layers = [prob_network.output_layer] if feature_network is not None: out_layers.append(feature_network.output_layer) LayersPowered.__init__(self, out_layers)
def build_net(self, trainable=True, name=None): """ Set up q network based on class attributes. This function uses layers defined in garage.tf. Args: reuse: A bool indicates whether reuse variables in the same scope. trainable: A bool indicates whether variables are trainable. """ input_shape = self._env_spec.observation_space.shape assert len(input_shape) in [2, 3] if len(input_shape) == 2: input_shape = (1, ) + input_shape with tf.variable_scope(name): l_in = layers.InputLayer(shape=(None, self._obs_dim), name="obs") l_hid = layers.reshape(l_in, ([0], ) + input_shape, name="reshape_input") if self._batch_norm: l_hid = layers.batch_norm(l_hid) for idx, conv_filter, filter_size, stride, pad in zip( range(len(self._conv_filters)), self._conv_filters, self._conv_filter_sizes, self._conv_strides, self._conv_pads, ): l_hid = layers.Conv2DLayer( l_hid, num_filters=conv_filter, filter_size=filter_size, stride=(stride, stride), pad=pad, nonlinearity=self._hidden_nonlinearity, name="conv_hidden_%d" % idx, weight_normalization=self._weight_normalization, trainable=trainable, ) if self._pooling: l_hid = layers.Pool2DLayer(l_hid, pool_size=self._pool_size) if self._batch_norm: l_hid = layers.batch_norm(l_hid) l_hid = layers.flatten(l_hid, name="conv_flatten") l_action = layers.InputLayer(shape=(None, self._action_dim), name="actions") n_layers = len(self._hidden_sizes) + 1 if n_layers > 1: action_merge_layer = \ (self._action_merge_layer % n_layers + n_layers) % n_layers else: action_merge_layer = 1 for idx, size in enumerate(self._hidden_sizes): if self._batch_norm: l_hid = batch_norm(l_hid) if idx == action_merge_layer: l_hid = layers.ConcatLayer([l_hid, l_action]) l_hid = layers.DenseLayer( l_hid, num_units=size, nonlinearity=self._hidden_nonlinearity, trainable=trainable, name="hidden_%d" % (idx + 1)) if action_merge_layer == n_layers: l_hid = layers.ConcatLayer([l_hid, l_action]) l_output = layers.DenseLayer( l_hid, num_units=1, nonlinearity=self._output_nonlinearity, trainable=trainable, name="output") output_var = layers.get_output(l_output) f_qval = tensor_utils.compile_function( [l_in.input_var, l_action.input_var], output_var) output_layer = l_output obs_layer = l_in action_layer = l_action return f_qval, output_layer, obs_layer, action_layer
def __init__( self, env_spec, name='GaussianLSTMPolicy', hidden_dim=32, hidden_nonlinearity=tf.tanh, recurrent_nonlinearity=tf.nn.sigmoid, recurrent_w_x_init=L.XavierUniformInitializer(), recurrent_w_h_init=L.OrthogonalInitializer(), output_nonlinearity=None, output_w_init=L.XavierUniformInitializer(), feature_network=None, state_include_action=True, learn_std=True, init_std=1.0, lstm_layer_cls=L.LSTMLayer, use_peepholes=False, std_share_network=False, ): """ :param env_spec: A spec for the env. :param hidden_dim: dimension of hidden layer :param hidden_nonlinearity: nonlinearity used for each hidden layer :return: """ assert isinstance(env_spec.action_space, akro.Box) self._mean_network_name = 'mean_network' self._std_network_name = 'std_network' with tf.variable_scope(name, 'GaussianLSTMPolicy'): Serializable.quick_init(self, locals()) super(GaussianLSTMPolicy, self).__init__(env_spec) obs_dim = env_spec.observation_space.flat_dim action_dim = env_spec.action_space.flat_dim if state_include_action: input_dim = obs_dim + action_dim else: input_dim = obs_dim l_input = L.InputLayer(shape=(None, None, input_dim), name='input') if feature_network is None: feature_dim = input_dim l_flat_feature = None l_feature = l_input else: feature_dim = feature_network.output_layer.output_shape[-1] l_flat_feature = feature_network.output_layer l_feature = L.OpLayer( l_flat_feature, extras=[l_input], name='reshape_feature', op=lambda flat_feature, input: tf.reshape( flat_feature, tf.stack([ tf.shape(input)[0], tf.shape(input)[1], feature_dim ])), shape_op=lambda _, input_shape: (input_shape[0], input_shape[1], feature_dim)) if std_share_network: mean_network = LSTMNetwork( input_shape=(feature_dim, ), input_layer=l_feature, output_dim=2 * action_dim, hidden_dim=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, recurrent_nonlinearity=recurrent_nonlinearity, recurrent_w_x_init=recurrent_w_x_init, recurrent_w_h_init=recurrent_w_h_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, lstm_layer_cls=lstm_layer_cls, name='lstm_mean_network', use_peepholes=use_peepholes, ) l_mean = L.SliceLayer( mean_network.output_layer, slice(action_dim), name='mean_slice', ) l_step_mean = L.SliceLayer( mean_network.step_output_layer, slice(action_dim), name='step_mean_slice', ) l_log_std = L.SliceLayer( mean_network.output_layer, slice(action_dim, 2 * action_dim), name='log_std_slice', ) l_step_log_std = L.SliceLayer( mean_network.step_output_layer, slice(action_dim, 2 * action_dim), name='step_log_std_slice', ) else: mean_network = LSTMNetwork( input_shape=(feature_dim, ), input_layer=l_feature, output_dim=action_dim, hidden_dim=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, recurrent_nonlinearity=recurrent_nonlinearity, recurrent_w_x_init=recurrent_w_x_init, recurrent_w_h_init=recurrent_w_h_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, lstm_layer_cls=lstm_layer_cls, name='lstm_mean_network', use_peepholes=use_peepholes, ) l_mean = mean_network.output_layer l_step_mean = mean_network.step_output_layer l_log_std = L.ParamLayer( mean_network.input_layer, num_units=action_dim, param=tf.constant_initializer(np.log(init_std)), name='output_log_std', trainable=learn_std, ) l_step_log_std = L.ParamLayer( mean_network.step_input_layer, num_units=action_dim, param=l_log_std.param, name='step_output_log_std', trainable=learn_std, ) self.mean_network = mean_network self.feature_network = feature_network self.l_input = l_input self.state_include_action = state_include_action self.name = name flat_input_var = tf.placeholder(dtype=tf.float32, shape=(None, input_dim), name='flat_input') if feature_network is None: feature_var = flat_input_var else: feature_var = L.get_output( l_flat_feature, {feature_network.input_layer: flat_input_var}) with tf.name_scope(self._mean_network_name, values=[feature_var]): (out_step_mean, out_step_hidden, out_mean_cell) = L.get_output( [ l_step_mean, mean_network.step_hidden_layer, mean_network.step_cell_layer ], {mean_network.step_input_layer: feature_var}) out_step_mean = tf.identity(out_step_mean, 'step_mean') out_step_hidden = tf.identity(out_step_hidden, 'step_hidden') out_mean_cell = tf.identity(out_mean_cell, 'mean_cell') with tf.name_scope(self._std_network_name, values=[feature_var]): out_step_log_std = L.get_output( l_step_log_std, {mean_network.step_input_layer: feature_var}) out_step_log_std = tf.identity(out_step_log_std, 'step_log_std') self.f_step_mean_std = tensor_utils.compile_function([ flat_input_var, mean_network.step_prev_state_layer.input_var, ], [ out_step_mean, out_step_log_std, out_step_hidden, out_mean_cell ]) self.l_mean = l_mean self.l_log_std = l_log_std self.input_dim = input_dim self.action_dim = action_dim self.hidden_dim = hidden_dim self.prev_actions = None self.prev_hiddens = None self.prev_cells = None self.dist = RecurrentDiagonalGaussian(action_dim) out_layers = [l_mean, l_log_std] if feature_network is not None: out_layers.append(feature_network.output_layer) LayersPowered.__init__(self, out_layers)
def build_net(self, trainable=True, name=None): """ Set up policy network based on class attributes. This function uses layers defined in garage.tf. Args: reuse: A bool indicates whether reuse variables in the same scope. trainable: A bool indicates whether variables are trainable. """ input_shape = self._env_spec.observation_space.shape assert len(input_shape) in [2, 3] if len(input_shape) == 2: input_shape = (1, ) + input_shape with tf.variable_scope(name): l_in = layers.InputLayer(shape=(None, self._obs_dim), name="obs") l_hid = layers.reshape( l_in, ([0], ) + input_shape, name="reshape_input") if self._batch_norm: l_hid = layers.batch_norm(l_hid) for idx, conv_filter, filter_size, stride, pad in zip( range(len(self._conv_filters)), self._conv_filters, self._conv_filter_sizes, self._conv_strides, self._conv_pads, ): l_hid = layers.Conv2DLayer( l_hid, num_filters=conv_filter, filter_size=filter_size, stride=(stride, stride), pad=pad, nonlinearity=self._hidden_nonlinearity, name="conv_hidden_%d" % idx, weight_normalization=self._weight_normalization, trainable=trainable, ) if self._pooling: l_hid = layers.Pool2DLayer(l_hid, pool_size=self._pool_size) if self._batch_norm: l_hid = layers.batch_norm(l_hid) l_hid = layers.flatten(l_hid, name="conv_flatten") for idx, hidden_size in enumerate(self._hidden_sizes): l_hid = layers.DenseLayer( l_hid, num_units=hidden_size, nonlinearity=self._hidden_nonlinearity, name="hidden_%d" % idx, weight_normalization=self._weight_normalization, trainable=trainable, ) if self._batch_norm: l_hid = layers.batch_norm(l_hid) l_output = layers.DenseLayer( l_hid, num_units=self._action_dim, nonlinearity=self._output_nonlinearity, name="output", weight_normalization=self._weight_normalization, trainable=trainable, ) with tf.name_scope(self._policy_network_name): action = layers.get_output(l_output) # scaled_action = tf.multiply( # action, self._action_bound, name="scaled_action") f_prob_online = tensor_utils.compile_function( inputs=[l_in.input_var], outputs=action) output_layer = l_output obs_layer = l_in return f_prob_online, output_layer, obs_layer
def __init__(self, input_shape, output_dim, hidden_dim, name=None, hidden_nonlinearity=tf.nn.relu, lstm_layer_cls=ly.LSTMLayer, output_nonlinearity=None, input_var=None, input_layer=None, forget_bias=1.0, use_peepholes=False, layer_args=None): with tf.variable_scope(name, "LSTMNetwork"): if input_layer is None: l_in = ly.InputLayer( shape=(None, None) + input_shape, input_var=input_var, name="input") else: l_in = input_layer l_step_input = ly.InputLayer( shape=(None, ) + input_shape, name="step_input") # contains previous hidden and cell state l_step_prev_state = ly.InputLayer( shape=(None, hidden_dim * 2), name="step_prev_state") if layer_args is None: layer_args = dict() l_lstm = lstm_layer_cls( l_in, num_units=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, hidden_init_trainable=False, name="lstm_layer", forget_bias=forget_bias, cell_init_trainable=False, use_peepholes=use_peepholes, **layer_args) l_lstm_flat = ly.ReshapeLayer( l_lstm, shape=(-1, hidden_dim), name="lstm_flat") l_output_flat = ly.DenseLayer( l_lstm_flat, num_units=output_dim, nonlinearity=output_nonlinearity, name="output_flat") l_output = ly.OpLayer( l_output_flat, op=lambda flat_output, l_input: tf.reshape( flat_output, tf.stack((tf.shape(l_input)[0], tf.shape(l_input)[1], -1)) ), shape_op=lambda flat_output_shape, l_input_shape: ( l_input_shape[0], l_input_shape[1], flat_output_shape[-1]), extras=[l_in], name="output") l_step_state = l_lstm.get_step_layer( l_step_input, l_step_prev_state, name="step_state") l_step_hidden = ly.SliceLayer( l_step_state, indices=slice(hidden_dim), name="step_hidden") l_step_cell = ly.SliceLayer( l_step_state, indices=slice(hidden_dim, None), name="step_cell") l_step_output = ly.DenseLayer( l_step_hidden, num_units=output_dim, nonlinearity=output_nonlinearity, w=l_output_flat.w, b=l_output_flat.b, name="step_output") self._l_in = l_in self._hid_init_param = l_lstm.h0 self._cell_init_param = l_lstm.c0 self._l_lstm = l_lstm self._l_out = l_output self._l_step_input = l_step_input self._l_step_prev_state = l_step_prev_state self._l_step_hidden = l_step_hidden self._l_step_cell = l_step_cell self._l_step_state = l_step_state self._l_step_output = l_step_output self._hidden_dim = hidden_dim
def __init__(self, input_shape, output_dim, hidden_dim, name=None, hidden_nonlinearity=tf.nn.relu, gru_layer_cls=ly.GRULayer, output_nonlinearity=None, input_var=None, input_layer=None, layer_args=None): with tf.variable_scope(name, "GRUNetwork"): if input_layer is None: l_in = ly.InputLayer( shape=(None, None) + input_shape, input_var=input_var, name="input") else: l_in = input_layer l_step_input = ly.InputLayer( shape=(None, ) + input_shape, name="step_input") l_step_prev_state = ly.InputLayer( shape=(None, hidden_dim), name="step_prev_state") if layer_args is None: layer_args = dict() l_gru = gru_layer_cls( l_in, num_units=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, hidden_init_trainable=False, name="gru", **layer_args) l_gru_flat = ly.ReshapeLayer( l_gru, shape=(-1, hidden_dim), name="gru_flat") l_output_flat = ly.DenseLayer( l_gru_flat, num_units=output_dim, nonlinearity=output_nonlinearity, name="output_flat") l_output = ly.OpLayer( l_output_flat, op=lambda flat_output, l_input: tf.reshape( flat_output, tf.stack((tf.shape(l_input)[0], tf.shape(l_input)[1], -1)) ), shape_op=lambda flat_output_shape, l_input_shape: ( l_input_shape[0], l_input_shape[1], flat_output_shape[-1]), extras=[l_in], name="output") l_step_state = l_gru.get_step_layer( l_step_input, l_step_prev_state, name="step_state") l_step_hidden = l_step_state l_step_output = ly.DenseLayer( l_step_hidden, num_units=output_dim, nonlinearity=output_nonlinearity, w=l_output_flat.w, b=l_output_flat.b, name="step_output") self._l_in = l_in self._hid_init_param = l_gru.h0 self._l_gru = l_gru self._l_out = l_output self._l_step_input = l_step_input self._l_step_prev_state = l_step_prev_state self._l_step_hidden = l_step_hidden self._l_step_state = l_step_state self._l_step_output = l_step_output self._hidden_dim = hidden_dim