baselines/deepq/build_graph.py
def build_train(make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=None, gamma=1.0,
    double_q=True, scope="deepq", reuse=None, param_noise=False, param_noise_filter_func=None):
    """Creates the train function:

    Parameters
    ----------
    make_obs_ph: str -> tf.placeholder or TfInput
        a function that takes a name and creates a placeholder of input with that name
    q_func: (tf.Variable, int, str, bool) -> tf.Variable
        the model that takes the following inputs:
            observation_in: object
                the output of observation placeholder
            num_actions: int
                number of actions
            scope: str
            reuse: bool
                should be passed to outer variable scope
        and returns a tensor of shape (batch_size, num_actions) with values of every action.
    num_actions: int
        number of actions
    reuse: bool
        whether or not to reuse the graph variables
    optimizer: tf.train.Optimizer
        optimizer to use for the Q-learning objective.
    grad_norm_clipping: float or None
        clip gradient norms to this value. If None no clipping is performed.
    gamma: float
        discount rate.
    double_q: bool
        if true will use Double Q Learning (https://arxiv.org/abs/1509.06461).
        In general it is a good idea to keep it enabled.
    scope: str or VariableScope
        optional scope for variable_scope.
    reuse: bool or None
        whether or not the variables should be reused. To be able to reuse the scope must be given.
    param_noise: bool
        whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
    param_noise_filter_func: tf.Variable -> bool
        function that decides whether or not a variable should be perturbed. Only applicable
        if param_noise is True. If set to None, default_param_noise_filter is used by default.

    Returns
    -------
    act: (tf.Variable, bool, float) -> tf.Variable
        function to select and action given observation.
`       See the top of the file for details.
    train: (object, np.array, np.array, object, np.array, np.array) -> np.array
        optimize the error in Bellman's equation.
`       See the top of the file for details.
    update_target: () -> ()
        copy the parameters from optimized Q function to the target Q function.
`       See the top of the file for details.
    debug: {str: function}
        a bunch of functions to print debug data like q_values.
    """
    if param_noise:
        act_f = build_act_with_param_noise(make_obs_ph, q_func, num_actions, scope=scope, reuse=reuse,
            param_noise_filter_func=param_noise_filter_func)
    else:
        act_f = build_act(make_obs_ph, q_func, num_actions, scope=scope, reuse=reuse)

    with tf.variable_scope(scope, reuse=reuse):
        # set up placeholders
        obs_t_input = make_obs_ph("obs_t")
        act_t_ph = tf.placeholder(tf.int32, [None], name="action")
        rew_t_ph = tf.placeholder(tf.float32, [None], name="reward")
        obs_tp1_input = make_obs_ph("obs_tp1")
        done_mask_ph = tf.placeholder(tf.float32, [None], name="done")
        importance_weights_ph = tf.placeholder(tf.float32, [None], name="weight")

        # q network evaluation
        q_t = q_func(obs_t_input.get(), num_actions, scope="q_func", reuse=True)  # reuse parameters from act
        q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/q_func")

        # target q network evalution
        q_tp1 = q_func(obs_tp1_input.get(), num_actions, scope="target_q_func")
        target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/target_q_func")

        # q scores for actions which we know were selected in the given state.
        q_t_selected = tf.reduce_sum(q_t * tf.one_hot(act_t_ph, num_actions), 1)

        # compute estimate of best possible value starting from state at t + 1
        if double_q:
            q_tp1_using_online_net = q_func(obs_tp1_input.get(), num_actions, scope="q_func", reuse=True)
            q_tp1_best_using_online_net = tf.argmax(q_tp1_using_online_net, 1)
            q_tp1_best = tf.reduce_sum(q_tp1 * tf.one_hot(q_tp1_best_using_online_net, num_actions), 1)
        else:
            q_tp1_best = tf.reduce_max(q_tp1, 1)
        q_tp1_best_masked = (1.0 - done_mask_ph) * q_tp1_best

        # compute RHS of bellman equation
        q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked

        # compute the error (potentially clipped)
        td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
        errors = U.huber_loss(td_error)
        weighted_error = tf.reduce_mean(importance_weights_ph * errors)

        # compute optimization op (potentially with gradient clipping)
        if grad_norm_clipping is not None:
            gradients = optimizer.compute_gradients(weighted_error, var_list=q_func_vars)
            for i, (grad, var) in enumerate(gradients):
                if grad is not None:
                    gradients[i] = (tf.clip_by_norm(grad, grad_norm_clipping), var)
            optimize_expr = optimizer.apply_gradients(gradients)
        else:
            optimize_expr = optimizer.minimize(weighted_error, var_list=q_func_vars)

        # update_target_fn will be called periodically to copy Q network to target Q network
        update_target_expr = []
        for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name),
                                   sorted(target_q_func_vars, key=lambda v: v.name)):
            update_target_expr.append(var_target.assign(var))
        update_target_expr = tf.group(*update_target_expr)

        # Create callable functions
        train = U.function(
            inputs=[
                obs_t_input,
                act_t_ph,
                rew_t_ph,
                obs_tp1_input,
                done_mask_ph,
                importance_weights_ph
            ],
            outputs=td_error,
            updates=[optimize_expr]
        )
        update_target = U.function([], [], updates=[update_target_expr])

        q_values = U.function([obs_t_input], q_t)

        return act_f, train, update_target, {'q_values': q_values}
def build_train(make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=None, gamma=1.0,
    double_q=True, scope="deepq", reuse=None, param_noise=False, param_noise_filter_func=None):
    """Creates the train function:

    Parameters
    ----------
    make_obs_ph: str -> tf.placeholder or TfInput
        a function that takes a name and creates a placeholder of input with that name
    q_func: (tf.Variable, int, str, bool) -> tf.Variable
        the model that takes the following inputs:
            observation_in: object
                the output of observation placeholder
            num_actions: int
                number of actions
            scope: str
            reuse: bool
                should be passed to outer variable scope
        and returns a tensor of shape (batch_size, num_actions) with values of every action.
    num_actions: int
        number of actions
    reuse: bool
        whether or not to reuse the graph variables
    optimizer: tf.train.Optimizer
        optimizer to use for the Q-learning objective.
    grad_norm_clipping: float or None
        clip gradient norms to this value. If None no clipping is performed.
    gamma: float
        discount rate.
    double_q: bool
        if true will use Double Q Learning (https://arxiv.org/abs/1509.06461).
        In general it is a good idea to keep it enabled.
    scope: str or VariableScope
        optional scope for variable_scope.
    reuse: bool or None
        whether or not the variables should be reused. To be able to reuse the scope must be given.
    param_noise: bool
        whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
    param_noise_filter_func: tf.Variable -> bool
        function that decides whether or not a variable should be perturbed. Only applicable
        if param_noise is True. If set to None, default_param_noise_filter is used by default.

    Returns
    -------
    act: (tf.Variable, bool, float) -> tf.Variable
        function to select and action given observation.
`       See the top of the file for details.
    train: (object, np.array, np.array, object, np.array, np.array) -> np.array
        optimize the error in Bellman's equation.
`       See the top of the file for details.
    update_target: () -> ()
        copy the parameters from optimized Q function to the target Q function.
`       See the top of the file for details.
    debug: {str: function}
        a bunch of functions to print debug data like q_values.
    """
    if param_noise:
        act_f = build_act_with_param_noise(make_obs_ph, q_func, num_actions, scope=scope, reuse=reuse,
            param_noise_filter_func=param_noise_filter_func)
    else:
        act_f = build_act(make_obs_ph, q_func, num_actions, scope=scope, reuse=reuse)

    with tf.variable_scope(scope, reuse=reuse):
        # set up placeholders
        obs_t_input = make_obs_ph("obs_t")
        act_t_ph = tf.placeholder(tf.int32, [None], name="action")
        rew_t_ph = tf.placeholder(tf.float32, [None], name="reward")
        obs_tp1_input = make_obs_ph("obs_tp1")
        done_mask_ph = tf.placeholder(tf.float32, [None], name="done")
        importance_weights_ph = tf.placeholder(tf.float32, [None], name="weight")

        # q network evaluation
        q_t = q_func(obs_t_input.get(), num_actions, scope="q_func", reuse=True)  # reuse parameters from act
        q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/q_func")

        # target q network evalution
        q_tp1 = q_func(obs_tp1_input.get(), num_actions, scope="target_q_func")
        target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/target_q_func")

        # q scores for actions which we know were selected in the given state.
        q_t_selected = tf.reduce_sum(q_t * tf.one_hot(act_t_ph, num_actions), 1)

        # compute estimate of best possible value starting from state at t + 1
        if double_q:
            q_tp1_using_online_net = q_func(obs_tp1_input.get(), num_actions, scope="q_func", reuse=True)
            q_tp1_best_using_online_net = tf.argmax(q_tp1_using_online_net, 1)
            q_tp1_best = tf.reduce_sum(q_tp1 * tf.one_hot(q_tp1_best_using_online_net, num_actions), 1)
        else:
            q_tp1_best = tf.reduce_max(q_tp1, 1)
        q_tp1_best_masked = (1.0 - done_mask_ph) * q_tp1_best

        # compute RHS of bellman equation
        q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked

        # compute the error (potentially clipped)
        td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
        errors = U.huber_loss(td_error)
        weighted_error = tf.reduce_mean(importance_weights_ph * errors)

        # compute optimization op (potentially with gradient clipping)
        if grad_norm_clipping is not None:
            gradients = optimizer.compute_gradients(weighted_error, var_list=q_func_vars)
            for i, (grad, var) in enumerate(gradients):
                if grad is not None:
                    gradients[i] = (tf.clip_by_norm(grad, grad_norm_clipping), var)
            optimize_expr = optimizer.apply_gradients(gradients)
        else:
            optimize_expr = optimizer.minimize(weighted_error, var_list=q_func_vars)

        # update_target_fn will be called periodically to copy Q network to target Q network
        update_target_expr = []
        for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name),
                                   sorted(target_q_func_vars, key=lambda v: v.name)):
            update_target_expr.append(var_target.assign(var))
        update_target_expr = tf.group(*update_target_expr)

        # Create callable functions
        train = U.function(
            inputs=[
                obs_t_input,
                act_t_ph,
                rew_t_ph,
                obs_tp1_input,
                done_mask_ph,
                importance_weights_ph
            ],
            outputs=td_error,
            updates=[optimize_expr]
        )
        update_target = U.function([], [], updates=[update_target_expr])

        q_values = U.function([obs_t_input], q_t)

        return act_f, train, update_target, {'q_values': q_values}
def build_train ( make_obs_ph , q_func , num_actions , optimizer , grad_norm_clipping = None , gamma = 1.0 , double_q = True , scope = "deepq" , reuse = None , param_noise = False , param_noise_filter_func = None ) : if param_noise : act_f = build_act_with_param_noise ( make_obs_ph , q_func , num_actions , scope = scope , reuse = reuse , param_noise_filter_func = param_noise_filter_func ) else : act_f = build_act ( make_obs_ph , q_func , num_actions , scope = scope , reuse = reuse ) with tf . variable_scope ( scope , reuse = reuse ) : # set up placeholders obs_t_input = make_obs_ph ( "obs_t" ) act_t_ph = tf . placeholder ( tf . int32 , [ None ] , name = "action" ) rew_t_ph = tf . placeholder ( tf . float32 , [ None ] , name = "reward" ) obs_tp1_input = make_obs_ph ( "obs_tp1" ) done_mask_ph = tf . placeholder ( tf . float32 , [ None ] , name = "done" ) importance_weights_ph = tf . placeholder ( tf . float32 , [ None ] , name = "weight" ) # q network evaluation q_t = q_func ( obs_t_input . get ( ) , num_actions , scope = "q_func" , reuse = True ) # reuse parameters from act q_func_vars = tf . get_collection ( tf . GraphKeys . GLOBAL_VARIABLES , scope = tf . get_variable_scope ( ) . name + "/q_func" ) # target q network evalution q_tp1 = q_func ( obs_tp1_input . get ( ) , num_actions , scope = "target_q_func" ) target_q_func_vars = tf . get_collection ( tf . GraphKeys . GLOBAL_VARIABLES , scope = tf . get_variable_scope ( ) . name + "/target_q_func" ) # q scores for actions which we know were selected in the given state. q_t_selected = tf . reduce_sum ( q_t * tf . one_hot ( act_t_ph , num_actions ) , 1 ) # compute estimate of best possible value starting from state at t + 1 if double_q : q_tp1_using_online_net = q_func ( obs_tp1_input . get ( ) , num_actions , scope = "q_func" , reuse = True ) q_tp1_best_using_online_net = tf . argmax ( q_tp1_using_online_net , 1 ) q_tp1_best = tf . reduce_sum ( q_tp1 * tf . one_hot ( q_tp1_best_using_online_net , num_actions ) , 1 ) else : q_tp1_best = tf . reduce_max ( q_tp1 , 1 ) q_tp1_best_masked = ( 1.0 - done_mask_ph ) * q_tp1_best # compute RHS of bellman equation q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked # compute the error (potentially clipped) td_error = q_t_selected - tf . stop_gradient ( q_t_selected_target ) errors = U . huber_loss ( td_error ) weighted_error = tf . reduce_mean ( importance_weights_ph * errors ) # compute optimization op (potentially with gradient clipping) if grad_norm_clipping is not None : gradients = optimizer . compute_gradients ( weighted_error , var_list = q_func_vars ) for i , ( grad , var ) in enumerate ( gradients ) : if grad is not None : gradients [ i ] = ( tf . clip_by_norm ( grad , grad_norm_clipping ) , var ) optimize_expr = optimizer . apply_gradients ( gradients ) else : optimize_expr = optimizer . minimize ( weighted_error , var_list = q_func_vars ) # update_target_fn will be called periodically to copy Q network to target Q network update_target_expr = [ ] for var , var_target in zip ( sorted ( q_func_vars , key = lambda v : v . name ) , sorted ( target_q_func_vars , key = lambda v : v . name ) ) : update_target_expr . append ( var_target . assign ( var ) ) update_target_expr = tf . group ( * update_target_expr ) # Create callable functions train = U . function ( inputs = [ obs_t_input , act_t_ph , rew_t_ph , obs_tp1_input , done_mask_ph , importance_weights_ph ] , outputs = td_error , updates = [ optimize_expr ] ) update_target = U . function ( [ ] , [ ] , updates = [ update_target_expr ] ) q_values = U . function ( [ obs_t_input ] , q_t ) return act_f , train , update_target , { 'q_values' : q_values }
Creates the train function:

    Parameters
    ----------
    make_obs_ph: str -> tf.placeholder or TfInput
        a function that takes a name and creates a placeholder of input with that name
    q_func: (tf.Variable, int, str, bool) -> tf.Variable
        the model that takes the following inputs:
            observation_in: object
                the output of observation placeholder
            num_actions: int
                number of actions
            scope: str
            reuse: bool
                should be passed to outer variable scope
        and returns a tensor of shape (batch_size, num_actions) with values of every action.
    num_actions: int
        number of actions
    reuse: bool
        whether or not to reuse the graph variables
    optimizer: tf.train.Optimizer
        optimizer to use for the Q-learning objective.
    grad_norm_clipping: float or None
        clip gradient norms to this value. If None no clipping is performed.
    gamma: float
        discount rate.
    double_q: bool
        if true will use Double Q Learning (https://arxiv.org/abs/1509.06461).
        In general it is a good idea to keep it enabled.
    scope: str or VariableScope
        optional scope for variable_scope.
    reuse: bool or None
        whether or not the variables should be reused. To be able to reuse the scope must be given.
    param_noise: bool
        whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
    param_noise_filter_func: tf.Variable -> bool
        function that decides whether or not a variable should be perturbed. Only applicable
        if param_noise is True. If set to None, default_param_noise_filter is used by default.

    Returns
    -------
    act: (tf.Variable, bool, float) -> tf.Variable
        function to select and action given observation.
`       See the top of the file for details.
    train: (object, np.array, np.array, object, np.array, np.array) -> np.array
        optimize the error in Bellman's equation.
`       See the top of the file for details.
    update_target: () -> ()
        copy the parameters from optimized Q function to the target Q function.
`       See the top of the file for details.
    debug: {str: function}
        a bunch of functions to print debug data like q_values.
Creates the train function :