def __init__(self, env_spec, name='ContinuousMLPPolicy', hidden_sizes=(64, 64), hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.tanh, output_w_init=tf.initializers.glorot_uniform(), output_b_init=tf.zeros_initializer(), layer_normalization=False): super().__init__(name, env_spec) action_dim = env_spec.action_space.flat_dim self._hidden_sizes = hidden_sizes self._hidden_nonlinearity = hidden_nonlinearity self._hidden_w_init = hidden_w_init self._hidden_b_init = hidden_b_init self._output_nonlinearity = output_nonlinearity self._output_w_init = output_w_init self._output_b_init = output_b_init self._layer_normalization = layer_normalization self.obs_dim = env_spec.observation_space.flat_dim self.model = MLPModel(output_dim=action_dim, name='MLPModel', hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization) self._initialize()
def __init__(self, env_spec, name='CategoricalMLPPolicy', hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.glorot_uniform_initializer(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.softmax, output_w_init=tf.glorot_uniform_initializer(), output_b_init=tf.zeros_initializer(), layer_normalization=False): assert isinstance(env_spec.action_space, akro.Discrete), ( 'CategoricalMLPPolicy only works with akro.Discrete action ' 'space.') super().__init__(name, env_spec) self.obs_dim = env_spec.observation_space.flat_dim self.action_dim = env_spec.action_space.n self.model = MLPModel(output_dim=self.action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization, name='MLPModel') self._initialize()
def test_output_values(self, output_dim, hidden_sizes): model = MLPModel(output_dim=output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=None, hidden_w_init=tf.ones_initializer(), output_w_init=tf.ones_initializer()) outputs = model.build(self.input_var) output = self.sess.run(outputs, feed_dict={self.input_var: self.obs}) expected_output = np.full([1, output_dim], 5 * np.prod(hidden_sizes)) assert np.array_equal(output, expected_output)
def __init__(self, env_spec, name='ContinuousMLPPolicy', hidden_sizes=(64, 64), hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.glorot_uniform_initializer(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.tanh, output_w_init=tf.glorot_uniform_initializer(), output_b_init=tf.zeros_initializer(), input_include_goal=False, layer_normalization=False): super().__init__(name, env_spec) action_dim = env_spec.action_space.flat_dim self._hidden_sizes = hidden_sizes self._hidden_nonlinearity = hidden_nonlinearity self._hidden_w_init = hidden_w_init self._hidden_b_init = hidden_b_init self._output_nonlinearity = output_nonlinearity self._output_w_init = output_w_init self._output_b_init = output_b_init self._input_include_goal = input_include_goal self._layer_normalization = layer_normalization if self._input_include_goal: self.obs_dim = env_spec.observation_space.flat_dim_with_keys( ['observation', 'desired_goal']) else: self.obs_dim = env_spec.observation_space.flat_dim self.model = MLPModel(output_dim=action_dim, name='MLPModel', hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization) self._initialize()
def __init__(self, env_spec, name=None, hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.glorot_uniform_initializer(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.glorot_uniform_initializer(), output_b_init=tf.zeros_initializer(), dueling=False, layer_normalization=False): super().__init__(name) self._env_spec = env_spec self._hidden_sizes = hidden_sizes self._hidden_nonlinearity = hidden_nonlinearity self._hidden_w_init = hidden_w_init self._hidden_b_init = hidden_b_init self._output_nonlinearity = output_nonlinearity self._output_w_init = output_w_init self._output_b_init = output_b_init self._dueling = dueling self._layer_normalization = layer_normalization self.obs_dim = env_spec.observation_space.shape action_dim = env_spec.action_space.flat_dim if not dueling: self.model = MLPModel(output_dim=action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization) else: self.model = MLPDuelingModel( output_dim=action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization) self._initialize()
def test_is_pickleable(self, output_dim, hidden_sizes): model = MLPModel(output_dim=output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=None, hidden_w_init=tf.ones_initializer(), output_w_init=tf.ones_initializer()) outputs = model.build(self.input_var) # assign bias to all one with tf.compat.v1.variable_scope('MLPModel/mlp', reuse=True): bias = tf.compat.v1.get_variable('hidden_0/bias') bias.load(tf.ones_like(bias).eval()) output1 = self.sess.run(outputs, feed_dict={self.input_var: self.obs}) h = pickle.dumps(model) with tf.compat.v1.Session(graph=tf.Graph()) as sess: input_var = tf.compat.v1.placeholder(tf.float32, shape=(None, 5)) model_pickled = pickle.loads(h) outputs = model_pickled.build(input_var) output2 = sess.run(outputs, feed_dict={input_var: self.obs}) assert np.array_equal(output1, output2)
def __init__(self, env_spec, conv_filters, conv_filter_sizes, conv_strides, conv_pad, name='CategoricalCNNPolicy', hidden_sizes=[], hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.glorot_uniform_initializer(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.softmax, output_w_init=tf.glorot_uniform_initializer(), output_b_init=tf.zeros_initializer(), layer_normalization=False): assert isinstance(env_spec.action_space, akro.Discrete), ( 'CategoricalCNNPolicy only works with akro.Discrete action ' 'space.') super().__init__(name, env_spec) self.obs_dim = env_spec.observation_space.shape self.action_dim = env_spec.action_space.n self.model = Sequential( CNNModel(filter_dims=conv_filter_sizes, num_filters=conv_filters, strides=conv_strides, padding=conv_pad, hidden_nonlinearity=hidden_nonlinearity, name='CNNModel'), MLPModel(output_dim=self.action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization, name='MLPModel')) self._initialize()
class ContinuousMLPPolicy(Policy): """Continuous MLP Policy Network. The policy network selects action based on the state of the environment. It uses neural nets to fit the function of pi(s). Args: env_spec (metarl.envs.env_spec.EnvSpec): Environment specification. name (str): Policy name, also the variable scope. hidden_sizes (list[int]): Output dimension of dense layer(s). For example, (32, 32) means the MLP of this policy consists of two hidden layers, each with 32 hidden units. hidden_nonlinearity (callable): Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation. hidden_w_init (callable): Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor. hidden_b_init (callable): Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor. output_nonlinearity (callable): Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation. output_w_init (callable): Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor. output_b_init (callable): Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor. layer_normalization (bool): Bool for using layer normalization or not. """ def __init__(self, env_spec, name='ContinuousMLPPolicy', hidden_sizes=(64, 64), hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.tanh, output_w_init=tf.initializers.glorot_uniform(), output_b_init=tf.zeros_initializer(), layer_normalization=False): super().__init__(name, env_spec) action_dim = env_spec.action_space.flat_dim self._hidden_sizes = hidden_sizes self._hidden_nonlinearity = hidden_nonlinearity self._hidden_w_init = hidden_w_init self._hidden_b_init = hidden_b_init self._output_nonlinearity = output_nonlinearity self._output_w_init = output_w_init self._output_b_init = output_b_init self._layer_normalization = layer_normalization self.obs_dim = env_spec.observation_space.flat_dim self.model = MLPModel(output_dim=action_dim, name='MLPModel', hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization) self._initialize() def _initialize(self): state_input = tf.compat.v1.placeholder(tf.float32, shape=(None, self.obs_dim)) with tf.compat.v1.variable_scope(self.name) as vs: self._variable_scope = vs self.model.build(state_input) self._f_prob = tf.compat.v1.get_default_session().make_callable( self.model.networks['default'].outputs, feed_list=[self.model.networks['default'].input]) def get_action_sym(self, obs_var, name=None): """Symbolic graph of the action. Args: obs_var (tf.Tensor): Tensor input for symbolic graph. name (str): Name for symbolic graph. Returns: tf.Tensor: symbolic graph of the action. """ with tf.compat.v1.variable_scope(self._variable_scope): return self.model.build(obs_var, name=name) def get_action(self, observation): """Get single action from this policy for the input observation. Args: observation (numpy.ndarray): Observation from environment. Returns: numpy.ndarray: Predicted action. dict: Empty dict since this policy does not model a distribution. """ action = self._f_prob([observation]) action = self.action_space.unflatten(action) return action, dict() def get_actions(self, observations): """Get multiple actions from this policy for the input observations. Args: observations (numpy.ndarray): Observations from environment. Returns: numpy.ndarray: Predicted actions. dict: Empty dict since this policy does not model a distribution. """ actions = self._f_prob(observations) actions = self.action_space.unflatten_n(actions) return actions, dict() def get_regularizable_vars(self): """Get regularizable weight variables under the Policy scope. Returns: list(tf.Variable): List of regularizable variables. """ trainable = self.get_trainable_vars() return [ var for var in trainable if 'hidden' in var.name and 'kernel' in var.name ] @property def vectorized(self): """Vectorized or not. Returns: bool: vectorized or not. """ return True def clone(self, name): """Return a clone of the policy. It only copies the configuration of the Q-function, not the parameters. Args: name (str): Name of the newly created policy. Returns: metarl.tf.policies.ContinuousMLPPolicy: Clone of this object """ return self.__class__(name=name, env_spec=self._env_spec, hidden_sizes=self._hidden_sizes, hidden_nonlinearity=self._hidden_nonlinearity, hidden_w_init=self._hidden_w_init, hidden_b_init=self._hidden_b_init, output_nonlinearity=self._output_nonlinearity, output_w_init=self._output_w_init, output_b_init=self._output_b_init, layer_normalization=self._layer_normalization) def __getstate__(self): """Object.__getstate__. Returns: dict: the state to be pickled as the contents for the instance. """ new_dict = super().__getstate__() del new_dict['_f_prob'] return new_dict def __setstate__(self, state): """Object.__setstate__. Args: state (dict): unpickled state. """ super().__setstate__(state) self._initialize()
class CategoricalMLPPolicy(StochasticPolicy): """CategoricalMLPPolicy A policy that contains a MLP to make prediction based on a categorical distribution. It only works with akro.Discrete action space. Args: env_spec (metarl.envs.env_spec.EnvSpec): Environment specification. name (str): Policy name, also the variable scope. hidden_sizes (list[int]): Output dimension of dense layer(s). For example, (32, 32) means the MLP of this policy consists of two hidden layers, each with 32 hidden units. hidden_nonlinearity (callable): Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation. hidden_w_init (callable): Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor. hidden_b_init (callable): Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor. output_nonlinearity (callable): Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation. output_w_init (callable): Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor. output_b_init (callable): Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor. layer_normalization (bool): Bool for using layer normalization or not. """ def __init__(self, env_spec, name='CategoricalMLPPolicy', hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.glorot_uniform_initializer(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.softmax, output_w_init=tf.glorot_uniform_initializer(), output_b_init=tf.zeros_initializer(), layer_normalization=False): assert isinstance(env_spec.action_space, akro.Discrete), ( 'CategoricalMLPPolicy only works with akro.Discrete action ' 'space.') super().__init__(name, env_spec) self.obs_dim = env_spec.observation_space.flat_dim self.action_dim = env_spec.action_space.n self.model = MLPModel(output_dim=self.action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization, name='MLPModel') self._initialize() def _initialize(self): state_input = tf.compat.v1.placeholder(tf.float32, shape=(None, self.obs_dim)) with tf.compat.v1.variable_scope(self.name) as vs: self._variable_scope = vs self.model.build(state_input) self._f_prob = tf.compat.v1.get_default_session().make_callable( self.model.networks['default'].outputs, feed_list=[self.model.networks['default'].input]) @property def vectorized(self): """Vectorized or not.""" return True def dist_info_sym(self, obs_var, state_info_vars=None, name=None): """Symbolic graph of the distribution.""" with tf.compat.v1.variable_scope(self._variable_scope): prob = self.model.build(obs_var, name=name) return dict(prob=prob) def dist_info(self, obs, state_infos=None): """Distribution info.""" prob = self._f_prob(obs) return dict(prob=prob) def get_action(self, observation): """Return a single action.""" flat_obs = self.observation_space.flatten(observation) prob = self._f_prob([flat_obs])[0] action = self.action_space.weighted_sample(prob) return action, dict(prob=prob) def get_actions(self, observations): """Return multiple actions.""" flat_obs = self.observation_space.flatten_n(observations) probs = self._f_prob(flat_obs) actions = list(map(self.action_space.weighted_sample, probs)) return actions, dict(prob=probs) def get_regularizable_vars(self): """Get regularizable weight variables under the Policy scope.""" trainable = self.get_trainable_vars() return [ var for var in trainable if 'hidden' in var.name and 'kernel' in var.name ] @property def distribution(self): """Policy distribution.""" return Categorical(self.action_dim) def __getstate__(self): """Object.__getstate__.""" new_dict = super().__getstate__() del new_dict['_f_prob'] return new_dict def __setstate__(self, state): """Object.__setstate__.""" super().__setstate__(state) self._initialize()
def __init__(self, env_spec, filters, strides, hidden_sizes=(256, ), name=None, padding='SAME', max_pooling=False, pool_strides=(2, 2), pool_shapes=(2, 2), cnn_hidden_nonlinearity=tf.nn.relu, hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.initializers.glorot_uniform(), output_b_init=tf.zeros_initializer(), dueling=False, layer_normalization=False): if not isinstance(env_spec.observation_space, akro.Box) or \ not len(env_spec.observation_space.shape) in (2, 3): raise ValueError( '{} can only process 2D, 3D akro.Image or' ' akro.Box observations, but received an env_spec with ' 'observation_space of type {} and shape {}'.format( type(self).__name__, type(env_spec.observation_space).__name__, env_spec.observation_space.shape)) super().__init__(name) self._env_spec = env_spec self._action_dim = env_spec.action_space.n self._filters = filters self._strides = strides self._hidden_sizes = hidden_sizes self._padding = padding self._max_pooling = max_pooling self._pool_strides = pool_strides self._pool_shapes = pool_shapes self._cnn_hidden_nonlinearity = cnn_hidden_nonlinearity self._hidden_nonlinearity = hidden_nonlinearity self._hidden_w_init = hidden_w_init self._hidden_b_init = hidden_b_init self._output_nonlinearity = output_nonlinearity self._output_w_init = output_w_init self._output_b_init = output_b_init self._layer_normalization = layer_normalization self._dueling = dueling self.obs_dim = self._env_spec.observation_space.shape action_dim = self._env_spec.action_space.flat_dim if not max_pooling: cnn_model = CNNModel(filters=filters, strides=strides, padding=padding, hidden_nonlinearity=cnn_hidden_nonlinearity) else: cnn_model = CNNModelWithMaxPooling( filters=filters, strides=strides, padding=padding, pool_strides=pool_strides, pool_shapes=pool_shapes, hidden_nonlinearity=cnn_hidden_nonlinearity) if not dueling: output_model = MLPModel(output_dim=action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization) else: output_model = MLPDuelingModel( output_dim=action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization) self.model = Sequential(cnn_model, output_model) self._initialize()
def __init__(self, env_spec, filter_dims, num_filters, strides, hidden_sizes=[256], name=None, padding='SAME', max_pooling=False, pool_strides=(2, 2), pool_shapes=(2, 2), cnn_hidden_nonlinearity=tf.nn.relu, hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.glorot_uniform_initializer(), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.glorot_uniform_initializer(), output_b_init=tf.zeros_initializer(), dueling=False, layer_normalization=False): super().__init__(name) self._env_spec = env_spec self._action_dim = env_spec.action_space.n self._filter_dims = filter_dims self._num_filters = num_filters self._strides = strides self._hidden_sizes = hidden_sizes self._padding = padding self._max_pooling = max_pooling self._pool_strides = pool_strides self._pool_shapes = pool_shapes self._cnn_hidden_nonlinearity = cnn_hidden_nonlinearity self._hidden_nonlinearity = hidden_nonlinearity self._hidden_w_init = hidden_w_init self._hidden_b_init = hidden_b_init self._output_nonlinearity = output_nonlinearity self._output_w_init = output_w_init self._output_b_init = output_b_init self._layer_normalization = layer_normalization self._dueling = dueling self.obs_dim = self._env_spec.observation_space.shape action_dim = self._env_spec.action_space.flat_dim if not max_pooling: cnn_model = CNNModel(filter_dims=filter_dims, num_filters=num_filters, strides=strides, padding=padding, hidden_nonlinearity=cnn_hidden_nonlinearity) else: cnn_model = CNNModelWithMaxPooling( filter_dims=filter_dims, num_filters=num_filters, strides=strides, padding=padding, pool_strides=pool_strides, pool_shapes=pool_shapes, hidden_nonlinearity=cnn_hidden_nonlinearity) if not dueling: output_model = MLPModel(output_dim=action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization) else: output_model = MLPDuelingModel( output_dim=action_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=hidden_nonlinearity, hidden_w_init=hidden_w_init, hidden_b_init=hidden_b_init, output_nonlinearity=output_nonlinearity, output_w_init=output_w_init, output_b_init=output_b_init, layer_normalization=layer_normalization) self.model = Sequential(cnn_model, output_model) self._initialize()