Multi-actions vanilla policy Gradient

As a small extension to the previous policy gradient implementation we discussed, we are now going to study how to support multiple actions (ie. num_actions > 2) in the policy network.

  • The reference implementation is somewhat different from our previous model, since a dedicated agent class is introduced here:
    class agent():
        def __init__(self, lr, s_size,a_size,h_size):
            #These lines established the feed-forward part of the network. The agent takes a state and produces an action.
            self.state_in= tf.placeholder(shape=[None,s_size],dtype=tf.float32)
            hidden = slim.fully_connected(self.state_in,h_size,biases_initializer=None,activation_fn=tf.nn.relu)
            self.output = slim.fully_connected(hidden,a_size,activation_fn=tf.nn.softmax,biases_initializer=None)
            self.chosen_action = tf.argmax(self.output,1)
            #The next six lines establish the training proceedure. We feed the reward and chosen action into the network
            #to compute the loss, and use it to update the network.
            self.reward_holder = tf.placeholder(shape=[None],dtype=tf.float32)
            self.action_holder = tf.placeholder(shape=[None],dtype=tf.int32)
            self.indexes = tf.range(0, tf.shape(self.output)[0]) * tf.shape(self.output)[1] + self.action_holder
            self.responsible_outputs = tf.gather(tf.reshape(self.output, [-1]), self.indexes)
            self.loss = -tf.reduce_mean(tf.log(self.responsible_outputs)*self.reward_holder)
            tvars = tf.trainable_variables()
            self.gradient_holders = []
            for idx,var in enumerate(tvars):
                placeholder = tf.placeholder(tf.float32,name=str(idx)+'_holder')
            self.gradients = tf.gradients(self.loss,tvars)
            optimizer = tf.train.AdamOptimizer(learning_rate=lr)
            self.update_batch = optimizer.apply_gradients(zip(self.gradient_holders,tvars))
  • ⇒ But we are not going into such complication here, and I will just update the previous model instead, since the required change in itself is quite small: basically, all we need to do is to indroduce a softmax on multiple output neurons now.
  • On the selection of responsible weights: In the code above, we see that we are selecting the output weights that are directly responsible for the selected action to compute the loss:
            self.indexes = tf.range(0, tf.shape(self.output)[0]) * tf.shape(self.output)[1] + self.action_holder
            self.responsible_outputs = tf.gather(tf.reshape(self.output, [-1]), self.indexes)
            self.loss = -tf.reduce_mean(tf.log(self.responsible_outputs)*self.reward_holder)
    • First to compute the indices, we take the range of the output number of rows, so here this will be [0, batch_size-1],
    • Then we multiply this range by the number of cols in the output weight matrix, and that's the number of actions, so we get the values: [0, nactions, 2*nactions, …, (batch_size-1)*nactions]
    • And then we add the value of the “action_holder”, which will be in the range [0, nactions-1] ⇒ So we can see the list of indices as a list of pointers to the beginning of each row in the output matrix, and when we are the action_holder value to it, we move each of those pointers to the column corresponding to the action number. So in effect, we select all the weights participating in the prediction of the selected action for each observation. (Note that the “action_holder” will be a vector of length “batch_size” here).
    • Now, I actually not quite sure to understand why we would do that ? I mean, we have a softmax activation on the output layer, so from my perspective, this means that, when we make a decision to select a given action all weights are actually participating to make this decision… so we could just train all of them, no ? And in this case, we could simply use a cross entropy loss function and multiply that with the advantage value maybe ? (to be tested/investigated)
  • So after some refactoring of our previous version we now have:
    def train_policy_network_v2():
        logDEBUG("Building environment...")
        env = gym.make('CartPole-v0')
        # Hyperparameters
        H = 10 # number of hidden layer neurons
        batch_size = 5 # every how many episodes to do a param update?
        learning_rate = 1e-2 # feel free to play with this to train faster or more stably.
        gamma = 0.99 # discount factor for reward
        D = 4 # input dimensionality
        nactions = 2
        # Array of the possible actions:
        actions = np.arange(nactions)
        def discount_rewards(r):
            """ take 1D float array of rewards and compute discounted reward """
            discounted_r = np.zeros_like(r)
            running_add = 0
            for t in reversed(range(0, r.size)):
                running_add = running_add * gamma + r[t]
                discounted_r[t] = running_add
            return discounted_r
        # This defines the network as it goes from taking an observation of the environment to 
        # giving a probability of chosing to the action of moving left or right.
        observations = tf.placeholder(tf.float32, [None,D] , name="input_x")
        W1 = tf.get_variable("W1", shape=[D, H],
        layer1 = tf.nn.relu(tf.matmul(observations,W1))
        W2 = tf.get_variable("W2", shape=[H, nactions],
        score = tf.matmul(layer1,W2)
        # We build a softmax layer on top of the outputs:
        probability = tf.nn.softmax(score)
        # From here we define the parts of the network needed for learning a good policy.
        tvars = tf.trainable_variables()
        reward_holder = tf.placeholder(shape=[None],dtype=tf.float32)
        action_holder = tf.placeholder(shape=[None],dtype=tf.int32)
        indexes = tf.range(0, tf.shape(probability)[0]) * tf.shape(probability)[1] + action_holder
        responsible_outputs = tf.gather(tf.reshape(probability, [-1]), indexes)
        loss = -tf.reduce_mean(tf.log(responsible_outputs)*reward_holder)
        newGrads = tf.gradients(loss,tvars)
        # Once we have collected a series of gradients from multiple episodes, we apply them.
        # We don't just apply gradeients after every episode in order to account for noise in the reward signal.
        adam = tf.train.AdamOptimizer(learning_rate=learning_rate) # Our optimizer
        gradient_holders = []
        for idx,var in enumerate(tvars):
            placeholder = tf.placeholder(tf.float32,name=str(idx)+'_holder')
        updateGrads = adam.apply_gradients(zip(gradient_holders,tvars))
        # Running the training:
        xs,hs,dlogps,drs,ys,tfps = [],[],[],[],[],[]
        running_reward = None
        reward_sum = 0
        episode_number = 1
        total_episodes = 10000
        init = tf.global_variables_initializer()
        # Launch the graph
        with tf.Session() as sess:
            rendering = False
            observation = env.reset() # Obtain an initial observation of the environment
            # Reset the gradient placeholder. We will collect gradients in 
            # gradBuffer until we are ready to update our policy network. 
            gradBuffer =
            for ix,grad in enumerate(gradBuffer):
                gradBuffer[ix] = grad * 0
            while episode_number <= total_episodes:
                # Make sure the observation is in a shape the network can handle.
                x = np.reshape(observation,[1,D])
                # Probabilistically pick an action given our network outputs.
                probs =,feed_dict={observations:x})
                # logDEBUG("Probabilities: %s" % probs)
                action = np.random.choice(actions,p=probs[0])
                xs.append(x) # observation
                ys.append(action) # a "fake label"
                # step the environment and get new measurements
                observation, reward, done, info = env.step(action)
                reward_sum += reward
                drs.append(reward) # record reward (has to be done after we call step() to get reward for previous action)
                if done: 
                    episode_number += 1
                    # stack together all inputs, hidden states, action gradients, and rewards for this episode
                    epx = np.vstack(xs)
                    epy = np.vstack(ys)
                    epr = np.vstack(drs)
                    tfp = tfps
                    xs,hs,dlogps,drs,ys,tfps = [],[],[],[],[],[] # reset array memory
                    # compute the discounted reward backwards through time
                    discounted_epr = discount_rewards(epr)
                    # size the rewards to be unit normal (helps control the gradient estimator variance)
                    discounted_epr -= np.mean(discounted_epr)
                    discounted_epr /= np.std(discounted_epr)
                    # Get the gradient for this episode, and save it in the gradBuffer
                    # logDEBUG("epx shape: %s" % str(epx))
                    # logDEBUG("epy shape: %s" % str(epy))
                    # logDEBUG("epr shape: %s" % str(discounted_epr))
                    tGrad =,feed_dict={observations: epx, action_holder: epy.reshape(-1), reward_holder: discounted_epr.reshape(-1)})
                    for ix,grad in enumerate(tGrad):
                        gradBuffer[ix] += grad
                    # If we have completed enough episodes, then update the policy network with our gradients.
                    if episode_number % batch_size == 0: 
              ,feed_dict=dict(zip(gradient_holders, gradBuffer)))
                        for ix,grad in enumerate(gradBuffer):
                            gradBuffer[ix] = grad * 0
                        # Give a summary of how well our network is doing for each batch of episodes.
                        running_reward = reward_sum if running_reward is None else running_reward * 0.99 + reward_sum * 0.01
                        logDEBUG('%d/%d: Average reward for last %d episodes: %f. Total average reward %f.' % (episode_number, total_episodes,
                        batch_size, reward_sum//batch_size, running_reward//batch_size))
                        if reward_sum//batch_size >= 200: 
                            logDEBUG("Task solved in %d episodes!" % episode_number)
                        reward_sum = 0
                    observation = env.reset()
        logDEBUG("%d episodes completed." % episode_number)
  • And the training is working :-) So we are all good.
  • But, still, we have a warning from tensorflow:
    /mnt/array1/dev/projects/NervSeed/tools/linux/python-3.6/lib/python3.6/site-packages/tensorflow/python/ops/ UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
      "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
  • ⇒ I believe this warning could be due to the slicing we perform to retrieve the “responsible weights”. And as mentioned above, I have the feeling this algorithm should also work if we just use the tensorflow standard “cross_entropy” loss function with weights. So let's try that.
    • I replaced the code:
          indexes = tf.range(0, tf.shape(probability)[0]) * tf.shape(probability)[1] + action_holder
          responsible_outputs = tf.gather(tf.reshape(probability, [-1]), indexes)
          loss = -tf.reduce_mean(tf.log(responsible_outputs)*reward_holder)
    • With the new code:
    • ⇒ And Yes! This is still working just fine, and the tensorflow warning message is gone now. All right! :-)
  • blog/2019/0309_vanilla_policy_gradient.txt
  • Last modified: 2020/07/10 12:11
  • (external edit)