blog:2019:0309_model_based_policy_network

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—blog:2019:0309_model_based_policy_network [2020/07/10 12:11] (current) – created - external edit 127.0.0.1
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 +====== Model-based policy networks ======
 +
 +{{tag>deep_learning}}
 +
 +Continuing on our reinforcement learning path, we will consider here how to build a "model for our environment" and then use this model to train our policy network, instead of the "actual environment".
 +
 +====== ======
 +
 +===== References =====
 +
 +  * [[https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-3-model-based-rl-9a6fe0cce99|Simple Reinforcement Learning with Tensorflow: Part 3 - Model-Based RL]]
 +  * https://github.com/awjuliani/DeepRL-Agents/blob/master/Model-Network.ipynb
 +
 +===== Initial implementation =====
 +
 +  * So our reference implementation is here: <sxh python>import numpy as np
 +import random
 +import gym
 +
 +import tensorflow as tf
 +import tensorflow.contrib.slim as slim
 +import numpy as np
 +
 +from nv.core.utils import *
 +
 +# cf. https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-3-model-based-rl-9a6fe0cce99
 +
 +def train_model_policy_network():
 +    logDEBUG("Building environment...")
 +    env = gym.make('CartPole-v0')
 +    
 +    # hyperparameters
 +    H = 8 # number of hidden layer neurons
 +    learning_rate = 1e-2
 +    gamma = 0.99 # discount factor for reward
 +    decay_rate = 0.99 # decay factor for RMSProp leaky sum of grad^2
 +    resume = False # resume from previous checkpoint?
 +
 +    model_bs = 3 # Batch size when learning from model
 +    real_bs = 3 # Batch size when learning from real environment
 +
 +    # model initialization
 +    D = 4 # input dimensionality
 +
 +    # Policy network:
 +    tf.reset_default_graph()
 +    observations = tf.placeholder(tf.float32, [None,4] , name="input_x")
 +    W1 = tf.get_variable("W1", shape=[4, H],
 +            initializer=tf.contrib.layers.xavier_initializer())
 +    layer1 = tf.nn.relu(tf.matmul(observations,W1))
 +    W2 = tf.get_variable("W2", shape=[H, 1],
 +            initializer=tf.contrib.layers.xavier_initializer())
 +    score = tf.matmul(layer1,W2)
 +    probability = tf.nn.sigmoid(score)
 +
 +    tvars = tf.trainable_variables()
 +    input_y = tf.placeholder(tf.float32,[None,1], name="input_y")
 +    advantages = tf.placeholder(tf.float32,name="reward_signal")
 +    
 +    adam = tf.train.AdamOptimizer(learning_rate=learning_rate)
 +    W1Grad = tf.placeholder(tf.float32,name="batch_grad1")
 +    W2Grad = tf.placeholder(tf.float32,name="batch_grad2")
 +    batchGrad = [W1Grad,W2Grad]
 +    loglik = tf.log(input_y*(input_y - probability) + (1 - input_y)*(input_y + probability))
 +    loss = -tf.reduce_mean(loglik * advantages) 
 +    newGrads = tf.gradients(loss,tvars)
 +    updateGrads = adam.apply_gradients(zip(batchGrad,tvars))
 +
 +
 +    # Model network:
 +    mH = 256 # model layer size
 +
 +    input_data = tf.placeholder(tf.float32, [None, 5])
 +    with tf.variable_scope('rnnlm'):
 +        softmax_w = tf.get_variable("softmax_w", [mH, 50])
 +        softmax_b = tf.get_variable("softmax_b", [50])
 +
 +    previous_state = tf.placeholder(tf.float32, [None,5] , name="previous_state")
 +    W1M = tf.get_variable("W1M", shape=[5, mH],
 +            initializer=tf.contrib.layers.xavier_initializer())
 +    B1M = tf.Variable(tf.zeros([mH]),name="B1M")
 +    layer1M = tf.nn.relu(tf.matmul(previous_state,W1M) + B1M)
 +    W2M = tf.get_variable("W2M", shape=[mH, mH],
 +            initializer=tf.contrib.layers.xavier_initializer())
 +    B2M = tf.Variable(tf.zeros([mH]),name="B2M")
 +    layer2M = tf.nn.relu(tf.matmul(layer1M,W2M) + B2M)
 +    wO = tf.get_variable("wO", shape=[mH, 4],
 +            initializer=tf.contrib.layers.xavier_initializer())
 +    wR = tf.get_variable("wR", shape=[mH, 1],
 +            initializer=tf.contrib.layers.xavier_initializer())
 +    wD = tf.get_variable("wD", shape=[mH, 1],
 +            initializer=tf.contrib.layers.xavier_initializer())
 +
 +    bO = tf.Variable(tf.zeros([4]),name="bO")
 +    bR = tf.Variable(tf.zeros([1]),name="bR")
 +    bD = tf.Variable(tf.ones([1]),name="bD")
 +
 +
 +    predicted_observation = tf.matmul(layer2M,wO,name="predicted_observation") + bO
 +    predicted_reward = tf.matmul(layer2M,wR,name="predicted_reward") + bR
 +    predicted_done = tf.sigmoid(tf.matmul(layer2M,wD,name="predicted_done") + bD)
 +
 +    true_observation = tf.placeholder(tf.float32,[None,4],name="true_observation")
 +    true_reward = tf.placeholder(tf.float32,[None,1],name="true_reward")
 +    true_done = tf.placeholder(tf.float32,[None,1],name="true_done")
 +
 +
 +    predicted_state = tf.concat([predicted_observation,predicted_reward,predicted_done],1)
 +
 +    observation_loss = tf.square(true_observation - predicted_observation)
 +
 +    reward_loss = tf.square(true_reward - predicted_reward)
 +
 +    done_loss = tf.multiply(predicted_done, true_done) + tf.multiply(1-predicted_done, 1-true_done)
 +    done_loss = -tf.log(done_loss)
 +
 +    model_loss = tf.reduce_mean(observation_loss + done_loss + reward_loss)
 +
 +    modelAdam = tf.train.AdamOptimizer(learning_rate=learning_rate)
 +    updateModel = modelAdam.minimize(model_loss)
 +
 +    # helper functions:
 +    def resetGradBuffer(gradBuffer):
 +        for ix,grad in enumerate(gradBuffer):
 +            gradBuffer[ix] = grad * 0
 +        return gradBuffer
 +            
 +    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 function uses our model to produce a new state when given a previous state and action
 +    def stepModel(sess, xs, action):
 +        toFeed = np.reshape(np.hstack([xs[-1][0],np.array(action)]),[1,5])
 +        myPredict = sess.run([predicted_state],feed_dict={previous_state: toFeed})
 +        # We should clip the mode reward here:
 +        reward = myPredict[0][:,4]
 +        if abs(reward)>1000.0:
 +            logDEBUG("Clipping model reward value %f" % reward)
 +            reward = np.clip(reward, -1000.0,1000.0)
 +
 +        observation = myPredict[0][:,0:4]
 +        observation[:,0] = np.clip(observation[:,0],-2.4,2.4)
 +        observation[:,2] = np.clip(observation[:,2],-0.4,0.4)
 +        doneP = np.clip(myPredict[0][:,5],0,1)
 +        if doneP > 0.1 or len(xs)>= 300:
 +            done = True
 +        else:
 +            done = False
 +        return observation, reward, done
 +
 +    # Training the model and policy together:
 +    xs,drs,ys,ds = [],[],[],[]
 +    running_reward = None
 +    reward_sum = 0
 +    episode_number = 1
 +    real_episodes = 1
 +    init = tf.global_variables_initializer()
 +    batch_size = real_bs
 +
 +    drawFromModel = False # When set to True, will use model for observations
 +    trainTheModel = True # Whether to train the model
 +    trainThePolicy = False # Whether to train the policy
 +    switch_point = 1
 +
 +    # Launch the graph
 +    with tf.Session() as sess:
 +        rendering = False
 +        sess.run(init)
 +        observation = env.reset()
 +        x = observation
 +        gradBuffer = sess.run(tvars)
 +        gradBuffer = resetGradBuffer(gradBuffer)
 +        
 +        while episode_number <= 5000:
 +            # Start displaying environment once performance is acceptably high.
 +            # No rendering support yet.
 +            # if (reward_sum/batch_size > 150 and drawFromModel == False) or rendering == True : 
 +            #     env.render()
 +            #     rendering = True
 +                
 +            x = np.reshape(observation,[1,4])
 +
 +            tfprob = sess.run(probability,feed_dict={observations: x})
 +            action = 1 if np.random.uniform() < tfprob else 0
 +
 +            # record various intermediates (needed later for backprop)
 +            xs.append(x) 
 +            y = 1 if action == 0 else 0 
 +            ys.append(y)
 +            
 +            # step the  model or real environment and get new measurements
 +            if drawFromModel == False:
 +                observation, reward, done, info = env.step(action)
 +                CHECK(reward <= 1000.0, "Invalid reward value from env: %f"%reward)
 +            else:
 +                observation, reward, done = stepModel(sess,xs,action)
 +                CHECK(reward <= 1000.0, "Invalid reward value from model: %f"%reward)
 +                    
 +            reward_sum += reward
 +            
 +            ds.append(done*1)
 +            drs.append(reward) # record reward (has to be done after we call step() to get reward for previous action)
 +
 +            if done: 
 +                
 +                if drawFromModel == False: 
 +                    real_episodes += 1
 +                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)
 +                epd = np.vstack(ds)
 +                xs,drs,ys,ds = [],[],[],[] # reset array memory
 +                
 +                if trainTheModel == True:
 +                    actions = np.array([np.abs(y-1) for y in epy][:-1])
 +                    state_prevs = epx[:-1,:]
 +                    state_prevs = np.hstack([state_prevs,actions])
 +                    state_nexts = epx[1:,:]
 +                    rewards = np.array(epr[1:,:])
 +                    dones = np.array(epd[1:,:])
 +                    state_nextsAll = np.hstack([state_nexts,rewards,dones])
 +
 +                    feed_dict={previous_state: state_prevs, true_observation: state_nexts,true_done:dones,true_reward:rewards}
 +                    loss,pState,_ = sess.run([model_loss,predicted_state,updateModel],feed_dict)
 +                if trainThePolicy == True:
 +                    discounted_epr = discount_rewards(epr).astype('float32')
 +                    discounted_epr -= np.mean(discounted_epr)
 +                    dev = np.std(discounted_epr)
 +                    if dev > 0.0:
 +                        discounted_epr /= dev
 +                    
 +                    tGrad = sess.run(newGrads,feed_dict={observations: epx, input_y: epy, advantages: discounted_epr})
 +                    
 +                    # If gradients becom too large, end training process
 +                    if np.sum(tGrad[0] == tGrad[0]) == 0:
 +                        break
 +                    for ix,grad in enumerate(tGrad):
 +                        gradBuffer[ix] += grad
 +                    
 +                if switch_point + batch_size == episode_number: 
 +                    switch_point = episode_number
 +                    if trainThePolicy == True:
 +                        sess.run(updateGrads,feed_dict={W1Grad: gradBuffer[0],W2Grad:gradBuffer[1]})
 +                        gradBuffer = resetGradBuffer(gradBuffer)
 +
 +                    running_reward = reward_sum if running_reward is None else running_reward * 0.99 + reward_sum * 0.01
 +                    if drawFromModel == False:
 +                        logDEBUG('World Perf: Episode %d, Reward %f, action: %d, mean reward %f.' % (real_episodes,reward_sum/real_bs,action, running_reward/real_bs))
 +                        if reward_sum/batch_size >= 200:
 +                            break
 +                    reward_sum = 0
 +
 +                    # Once the model has been trained on 100 episodes, we start alternating between training the policy
 +                    # from the model and training the model from the real environment.
 +                    if episode_number > 100:
 +                        drawFromModel = not drawFromModel
 +                        trainTheModel = not trainTheModel
 +                        trainThePolicy = not trainThePolicy
 +                
 +                if drawFromModel == True:
 +                    observation = np.random.uniform(-0.1,0.1,[4]) # Generate reasonable starting point
 +                    batch_size = model_bs
 +                else:
 +                    observation = env.reset()
 +                    batch_size = real_bs
 +
 +    logDEBUG("Done with %d real episodes."% real_episodes)
 +</sxh>
 +
 +  * This implementation works, except that I had to clamp the reward values predicted by the model network to some "reasonable value": <sxh python>    def stepModel(sess, xs, action):
 +        toFeed = np.reshape(np.hstack([xs[-1][0],np.array(action)]),[1,5])
 +        myPredict = sess.run([predicted_state],feed_dict={previous_state: toFeed})
 +        # We should clip the mode reward here:
 +        reward = myPredict[0][:,4]
 +        if abs(reward)>1000.0:
 +            logDEBUG("Clipping model reward value %f" % reward)
 +            reward = np.clip(reward, -1000.0,1000.0)
 +</sxh>
 +
 +  * Because, we currently get very large reward predictions some times, such as: <code>2019-03-09T21:46:49.130780 [DEBUG] Clipping model reward value 27099967488.000000
 +2019-03-09T21:46:49.132562 [DEBUG] Clipping model reward value 29887655936.000000
 +2019-03-09T21:46:49.186213 [DEBUG] World Perf: Episode 187, Reward 34.666667, action: 0, mean reward 1337.982666.
 +2019-03-09T21:46:49.268631 [DEBUG] World Perf: Episode 190, Reward 34.333333, action: 1, mean reward 1311.774658.</code>
 +  * And thus, without this clamping, the mean reward value gets completely "out of control"
 +
 +/*
 +2019-03-09T17:18:55.168245 [DEBUG] World Perf: Episode 850.000000. Reward 129.333333. action: 0.000000. mean reward 113.953552.
 +2019-03-09T17:18:55.630618 [DEBUG] World Perf: Episode 853.000000. Reward 138.666667. action: 0.000000. mean reward 89519814371966976.000000.
 +/mnt/array1/dev/projects/NervSeed/tools/linux/python-3.6/lib/python3.6/site-packages/numpy/core/_methods.py:116: RuntimeWarning: overflow encountered in multiply
 +  x = um.multiply(x, x, out=x)
 +2019-03-09T17:18:56.648155 [DEBUG] World Perf: Episode 856.000000. Reward 151.666667. action: 0.000000. mean reward 101388646698265757089792.000000.
 +*/
 +
 +===== Analysis =====
 +
 +  * Nothing new to say on the policy network: it's exactly the same as the one we used in the post on [[.0308_full_policy_gradient|Full policy Gradient agent]]
 +  * Let's rather focus on the model network.
 +  * Our input for this network has a dimension D=5
 +  * Actually, the following part of the code is useless (so I will remove it): <sxh python>    input_data = tf.placeholder(tf.float32, [None, 5])
 +    with tf.variable_scope('rnnlm'):
 +        softmax_w = tf.get_variable("softmax_w", [mH, 50])
 +        softmax_b = tf.get_variable("softmax_b", [50])
 +</sxh>
 +  * For the model, we use 2 fully connected ReLU layers with mH=256 units each
 +  * Then we get 3 different outputs from those layers: the predicted next observation, next reward and next done state.
 +  * Then we compute the loss for each of those outputs, and we sum them before we can do the optimization step.
 +
 +  * => The clever thing here is really that from a single network, we can consider that we "split the outputs" into conceptually separated components, which could be a very useful trick in general.
 +
 +  * Then for the actual training we train the model and the policy network separately: the model is trained from inputs from the real environement, whereas the policy is trained from inputs from our model network, nothing too fancy here.
 +
 +