如何使用 Tensorflow Optimizer 而无需在每次迭代后返回控制的强化学习程序中重新计算激活？

Question

EDIT(1/3/16): corresponding github issue

I'm using Tensorflow (Python interface) to implement a q-learning agent with function approximation trained using stochastic gradient descent .

At each iteration of the experiment, a step function in the agent is called that updates the parameters of the approximator based on the new reward and activation, and then chooses a new action to perform.

Here is the problem(with reinforcement learning jargon):

• The agent computes its state-action value predictions to choose an action.
• Then gives control back to another program that simulates a step in the environment.
• Now the agent's step function is called for the next iteration. I want to use Tensorflow's Optimizer class to compute the gradients for me. However, this requires both the state-action value predictions that I computed the last step AND their graph. So:
  • If I run the optimizer on the whole graph, then it has to recompute the state-action value predictions.
  • But, if I store the prediction (for the chosen action) as a variable, then feed it to the optimizer as a placeholder, it no longer has the graph necessary to compute the gradients.
  • I can't just run it all in the same sess.run() the statement, because I have to give up control and return the chosen action in order to get the next observation and reward (to use in the target for the loss function).

So, is there a way that I can (without reinforcement learning jargon):

1. Compute part of my graph, returning value1.
2. Return value1 to the calling program to compute value2
3. In the next iteration, use value2 to as part of my loss function for gradient descent WITHOUT recomputing the part of the graph that computes value1.

Of course, I've considered the obvious solutions:

1. Just hardcode the gradients: This would be easy for the really simple approximators I'm using now but would be really inconvenient if I were experimenting with different filters and activation functions in a big convolutional network. I'd really like to use the Optimizer class if possible.

2. Call the environment simulation from within the agent: This system does this, but it would make mine more complicated, and remove a lot of the modularity and structure. So, I don't want to do this.

I've read through the API and whitepaper several times, but can't seem to come up with a solution. I was trying to come up with some way to feed the target into a graph to calculate the gradients, but couldn't come up with a way to build that graph automatically.

If it turns out this isn't possible in TensorFlow yet, do you think it would be very complicated to implement this as a new operator? (I haven't used C++ in a couple of years, so the TensorFlow source looks a little intimidating.) Or would I be better off switching to something like Torch, which has the imperative differentiation Autograd, instead of symbolic differentiation?

Thanks for taking the time to help me out with this. I was trying to make this as concise as I could.

EDIT: After doing some further searching I came across this previously asked question . It's a little different than mine (they are trying to avoid updating an LSTM network twice every iteration in Torch), and doesn't have any answers yet.

Here is some code if that helps:

'''
-Q-Learning agent for a grid-world environment.
-Receives input as raw RGB pixel representation of the screen.
-Uses an artificial neural network function approximator with one hidden layer

2015 Jonathon Byrd
'''

import random
import sys
#import copy
from rlglue.agent.Agent import Agent
from rlglue.agent import AgentLoader as AgentLoader
from rlglue.types import Action
from rlglue.types import Observation

import tensorflow as tf
import numpy as np

world_size = (3,3)
total_spaces = world_size[0] * world_size[1]

class simple_agent(Agent):

    #Contants
    discount_factor = tf.constant(0.5, name="discount_factor")
    learning_rate = tf.constant(0.01, name="learning_rate")
    exploration_rate = tf.Variable(0.2, name="exploration_rate")  # used to be a constant :P
    hidden_layer_size = 12

    #Network Parameters - weights and biases
    W = [tf.Variable(tf.truncated_normal([total_spaces * 3, hidden_layer_size], stddev=0.1), name="layer_1_weights"), 
    tf.Variable(tf.truncated_normal([hidden_layer_size,4], stddev=0.1), name="layer_2_weights")]
    b = [tf.Variable(tf.zeros([hidden_layer_size]), name="layer_1_biases"), tf.Variable(tf.zeros([4]), name="layer_2_biases")]

    #Input placeholders - observation and reward
    screen = tf.placeholder(tf.float32, shape=[1, total_spaces * 3], name="observation") #input pixel rgb values
    reward = tf.placeholder(tf.float32, shape=[], name="reward")

    #last step data
    last_obs = np.array([1, 2, 3], ndmin=4)
    last_act = -1

    #Last step placeholders
    last_screen = tf.placeholder(tf.float32, shape=[1, total_spaces * 3], name="previous_observation")
    last_move = tf.placeholder(tf.int32, shape = [], name="previous_action")

    next_prediction = tf.placeholder(tf.float32, shape = [], name="next_prediction")

    step_count = 0

    def __init__(self):
        #Initialize computational graphs
        self.q_preds = self.Q(self.screen)
        self.last_q_preds = self.Q(self.last_screen)
        self.action = self.choose_action(self.q_preds)
        self.next_pred = self.max_q(self.q_preds)
        self.last_pred = self.act_to_pred(self.last_move, self.last_q_preds) # inefficient recomputation
        self.loss = self.error(self.last_pred, self.reward, self.next_prediction)
        self.train = self.learn(self.loss)
        #Summaries and Statistics
        tf.scalar_summary(['loss'], self.loss)
        tf.scalar_summary('reward', self.reward)
        #w_hist = tf.histogram_summary("weights", self.W[0])
        self.summary_op = tf.merge_all_summaries()
        self.sess = tf.Session()
        self.summary_writer = tf.train.SummaryWriter('tensorlogs', graph_def=self.sess.graph_def)


    def agent_init(self,taskSpec):
        print("agent_init called")
        self.sess.run(tf.initialize_all_variables())

    def agent_start(self,observation):
        #print("agent_start called, observation = {0}".format(observation.intArray))
        o = np.divide(np.reshape(np.asarray(observation.intArray), (1,total_spaces * 3)), 255)
        return self.control(o)

    def agent_step(self,reward, observation):
        #print("agent_step called, observation = {0}".format(observation.intArray))
        print("step, reward: {0}".format(reward))
        o = np.divide(np.reshape(np.asarray(observation.intArray), (1,total_spaces * 3)), 255)

        next_prediction = self.sess.run([self.next_pred], feed_dict={self.screen:o})[0]

        if self.step_count % 10 == 0:
            summary_str = self.sess.run([self.summary_op, self.train], 
                feed_dict={self.reward:reward, self.last_screen:self.last_obs, 
                self.last_move:self.last_act, self.next_prediction:next_prediction})[0]

            self.summary_writer.add_summary(summary_str, global_step=self.step_count)
        else:
            self.sess.run([self.train], 
                feed_dict={self.screen:o, self.reward:reward, self.last_screen:self.last_obs, 
                self.last_move:self.last_act, self.next_prediction:next_prediction})

        return self.control(o)

    def control(self, observation):
        results = self.sess.run([self.action], feed_dict={self.screen:observation})
        action = results[0]

        self.last_act = action
        self.last_obs = observation

        if (action==0):  # convert action integer to direction character
            action = 'u'
        elif (action==1):
            action = 'l'
        elif (action==2):
            action = 'r'
        elif (action==3):
            action = 'd'
        returnAction=Action()
        returnAction.charArray=[action]
        #print("return action returned {0}".format(action))
        self.step_count += 1
        return returnAction

    def Q(self, obs):  #calculates state-action value prediction with feed-forward neural net
        with tf.name_scope('network_inference') as scope:
            h1 = tf.nn.relu(tf.matmul(obs, self.W[0]) + self.b[0])
            q_preds = tf.matmul(h1, self.W[1]) + self.b[1] #linear activation
            return tf.reshape(q_preds, shape=[4])

    def choose_action(self, q_preds):  #chooses action epsilon-greedily
        with tf.name_scope('action_choice') as scope:
            exploration_roll = tf.random_uniform([])
            #greedy_action = tf.argmax(q_preds, 0)  # gets the action with the highest predicted Q-value
            #random_action = tf.cast(tf.floor(tf.random_uniform([], maxval=4.0)), tf.int64)

            #exploration rate updates
            #if self.step_count % 10000 == 0:
                #self.exploration_rate.assign(tf.div(self.exploration_rate, 2))

            return tf.select(tf.greater_equal(exploration_roll, self.exploration_rate), 
                tf.argmax(q_preds, 0),   #greedy_action
                tf.cast(tf.floor(tf.random_uniform([], maxval=4.0)), tf.int64))  #random_action

        '''
        Why does this return NoneType?:

        flag = tf.select(tf.greater_equal(exploration_roll, self.exploration_rate), 'g', 'r')
        if flag == 'g':  #greedy
            return tf.argmax(q_preds, 0) # gets the action with the highest predicted Q-value
        elif flag == 'r':  #random
            return tf.cast(tf.floor(tf.random_uniform([], maxval=4.0)), tf.int64)
        '''

    def error(self, last_pred, r, next_pred):
        with tf.name_scope('loss_function') as scope:
            y = tf.add(r, tf.mul(self.discount_factor, next_pred)) #target
            return tf.square(tf.sub(y, last_pred)) #squared difference error


    def learn(self, loss): #Update parameters using stochastic gradient descent
        #TODO:  Either figure out how to avoid computing the q-prediction twice or just hardcode the gradients.
        with tf.name_scope('train') as scope:
            return tf.train.GradientDescentOptimizer(self.learning_rate).minimize(loss, var_list=[self.W[0], self.W[1], self.b[0], self.b[1]])


    def max_q(self, q_preds):
        with tf.name_scope('greedy_estimate') as scope:
            return tf.reduce_max(q_preds)  #best predicted action from current state

    def act_to_pred(self, a, preds): #get the value prediction for action a
        with tf.name_scope('get_prediction') as scope:
            return tf.slice(preds, tf.reshape(a, shape=[1]), [1])


    def agent_end(self,reward):
        pass

    def agent_cleanup(self):
        self.sess.close()
        pass

    def agent_message(self,inMessage):
        if inMessage=="what is your name?":
            return "my name is simple_agent";
        else:
            return "I don't know how to respond to your message";

if __name__=="__main__":
    AgentLoader.loadAgent(simple_agent())

Answer 1

Right now what you want to do is very difficult in Tensorflow (0.6). Your best bet is to bite the bullet and call run multiple times at the cost of recomputing the activations. However, we are very aware of this issue internally. A prototype "partial run" solution is in the works, but there is no timeline for its completion right now. Since a truly satisfactory answer might require modifying tensorflow itself, you could also make a github issue for this and see if anyone else has anything to say on this there.

Edit: Experimental support for partial_run is now in. https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/client/session.py#L317