DQN with LSTMCell

Hello there I doing a project were we regulate temperature to a reference temperature.

I have current a DQN where i am trying to implement a LSTM layer so i know whether the temperature is going up or down. I have tried this here(the full code can have been uploaded<DRL.py>):


# AI for temperature regulator for pump

# Importing the libraries

import numpy as np
import random # random samples from different batches (experience replay)
import os # For loading and saving brain
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim # for using stochastic gradient descent
import torch.autograd as autograd # Conversion from tensor (advanced arrays) to avoid all that contains a gradient
# We want to put the tensor into a varaible taht will also contain a
# gradient and to this we need:
from torch.autograd import Variable
# to convert this tensor into a variable containing the tensor and the gradient

# Initializing and setting the variance of a tensor of weights
def normalized_columns_initializer(weights, std=1.0):
    out = torch.randn(weights.size())
    out *= std / torch.sqrt(out.pow(2).sum(1).expand_as(out)) # thanks to this initialization, we have var(out) = std^2
    return out

# Initializing the weights of the neural network in an optimal way for the learning
def weights_init(m):
    weight_shape = list(m.weight.data.size()) #?? list containing the shape of the weights in the object "m"
    fan_in = weight_shape[1] # dim1
    fan_out = weight_shape[0] # dim0
    w_bound = np.sqrt(6. / (fan_in + fan_out)) # weight bound
    m.weight.data.uniform_(-w_bound, w_bound) # generating some random weights of order inversely proportional to the size of the tensor of weights
    m.bias.data.fill_(0) # initializing all the bias with zeros

# Creating the architecture of the Neural Network
class Network(nn.Module): #inherinting from nn.Module
    
    #Self - refers to the object that will be created from this class
    #     - self here to specify that we're referring to the object
    def __init__(self, input_size, nb_action): #[self,input neuroner, output neuroner]
        super(Network, self).__init__() #inorder to use modules in torch.nn
        # Input and output neurons
        self.lstm = nn.LSTMCell(input_size, 30) # making an LSTM (Long Short Term Memory) to learn the temporal properties of the input
        self.fcL = nn.Linear(30, nb_action) # full connection of the
        print('hej12')
        self.apply(weights_init) # initilizing the weights of the model with random weights
        self.fcL.weight.data = normalized_columns_initializer(self.fcL.weight.data, 0.01) # setting the standard deviation of the fcL tensor of weights to 0.01
        self.fcL.bias.data.fill_(0) # initializing the actor bias with zeros
        self.lstm.bias_ih.data.fill_(0) # initializing the lstm bias with zeros
        self.lstm.bias_hh.data.fill_(0) # initializing the lstm bias with zeros
        self.train() # setting the module in "train" mode to activate the dropouts and batchnorms

		
		
    # For function that will activate neurons and perform forward propagation
    def forward(self, state):
        inputs, (hx, cx) = state # getting separately the input images to the tuple (hidden states, cell states)
        x = F.relu(self.lstm(inputs)) # forward propagating the signal from the input images to the 1st convolutional layer
        hx, cx = self.lstm(x, (hx, cx)) # the LSTM takes as input x and the old hidden & cell states and ouputs the new hidden & cell states
        x = hx # getting the useful output, which are the hidden states (principle of the LSTM)
        return self.fcL(x), (hx, cx) # returning the output of the actor (Q(S,A)), and the new hidden & cell states ((hx, cx))

I get the trace back from the function def weights_init
from
weight_shape = list(m.weight.data.size())
that
AttributeError: ‘LSTMCell’ object has no attribute ‘weight’

I have tried to get inspiration from this A3C code:

# AI for Breakout

# Importing the librairies
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

# Initializing and setting the variance of a tensor of weights
def normalized_columns_initializer(weights, std=1.0):
    out = torch.randn(weights.size())
    out *= std / torch.sqrt(out.pow(2).sum(1).expand_as(out)) # thanks to this initialization, we have var(out) = std^2
    return out

# Initializing the weights of the neural network in an optimal way for the learning
def weights_init(m):
    classname = m.__class__.__name__ # python trick that will look for the type of connection in the object "m" (convolution or full connection)
    if classname.find('Conv') != -1: # if the connection is a convolution
        weight_shape = list(m.weight.data.size()) # list containing the shape of the weights in the object "m"
        fan_in = np.prod(weight_shape[1:4]) # dim1 * dim2 * dim3
        fan_out = np.prod(weight_shape[2:4]) * weight_shape[0] # dim0 * dim2 * dim3
        w_bound = np.sqrt(6. / (fan_in + fan_out)) # weight bound
        m.weight.data.uniform_(-w_bound, w_bound) # generating some random weights of order inversely proportional to the size of the tensor of weights
        m.bias.data.fill_(0) # initializing all the bias with zeros
    elif classname.find('Linear') != -1: # if the connection is a full connection
        weight_shape = list(m.weight.data.size()) # list containing the shape of the weights in the object "m"
        fan_in = weight_shape[1] # dim1
        fan_out = weight_shape[0] # dim0
        w_bound = np.sqrt(6. / (fan_in + fan_out)) # weight bound
        m.weight.data.uniform_(-w_bound, w_bound) # generating some random weights of order inversely proportional to the size of the tensor of weights
        m.bias.data.fill_(0) # initializing all the bias with zeros

# Making the A3C brain

class ActorCritic(torch.nn.Module):

    def __init__(self, num_inputs, action_space):
        super(ActorCritic, self).__init__()
        self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1) # first convolution
        self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1) # second convolution
        self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1) # third convolution
        self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1) # fourth convolution
        self.lstm = nn.LSTMCell(32 * 3 * 3, 256) # making an LSTM (Long Short Term Memory) to learn the temporal properties of the input - we obtain a big encoded vector S of size 256 that encodes an event of the game
        num_outputs = action_space.n # getting the number of possible actions
        self.critic_linear = nn.Linear(256, 1) # full connection of the critic: output = V(S)
        self.actor_linear = nn.Linear(256, num_outputs) # full connection of the actor: output = Q(S,A)
        self.apply(weights_init) # initilizing the weights of the model with random weights
        self.actor_linear.weight.data = normalized_columns_initializer(self.actor_linear.weight.data, 0.01) # setting the standard deviation of the actor tensor of weights to 0.01
        self.actor_linear.bias.data.fill_(0) # initializing the actor bias with zeros
        self.critic_linear.weight.data = normalized_columns_initializer(self.critic_linear.weight.data, 1.0) # setting the standard deviation of the critic tensor of weights to 1
        self.critic_linear.bias.data.fill_(0) # initializing the critic bias with zeros
        self.lstm.bias_ih.data.fill_(0) # initializing the lstm bias with zeros
        self.lstm.bias_hh.data.fill_(0) # initializing the lstm bias with zeros
        self.train() # setting the module in "train" mode to activate the dropouts and batchnorms

    def forward(self, inputs):
        inputs, (hx, cx) = inputs # getting separately the input images to the tuple (hidden states, cell states)
        x = F.elu(self.conv1(inputs)) # forward propagating the signal from the input images to the 1st convolutional layer
        x = F.elu(self.conv2(x)) # forward propagating the signal from the 1st convolutional layer to the 2nd convolutional layer
        x = F.elu(self.conv3(x)) # forward propagating the signal from the 2nd convolutional layer to the 3rd convolutional layer
        x = F.elu(self.conv4(x)) # forward propagating the signal from the 3rd convolutional layer to the 4th convolutional layer
        x = x.view(-1, 32 * 3 * 3) # flattening the last convolutional layer into this 1D vector x
        hx, cx = self.lstm(x, (hx, cx)) # the LSTM takes as input x and the old hidden & cell states and ouputs the new hidden & cell states
        x = hx # getting the useful output, which are the hidden states (principle of the LSTM)
        return self.critic_linear(x), self.actor_linear(x), (hx, cx) # returning the output of the critic (V(S)), the output of the actor (Q(S,A)), and the new hidden & cell states ((hx, cx))

i Hope someone can help me because i dont understand why i get this error!

Best regards Søren Koch

The Initialization in the A3C code is only for Linear and Conv layers (notice the if classname.find conditions).

Thanks for giving the time!

but I also have a linear?

The fcL layer is linear, you should add the if classname.find('Linear') != -1: to the function.

thank you!

Now i ran into another problem… would you mind taking a look now you have been so awesome on this on?

Now it get:
RuntimeError: The expanded size of the tensor (30) must match the existing size (20) at non-singleton dimension 1. at c:\anaconda2\conda-bld\pytorch_1513133520683\work\torch\lib\th\generic/THTensor.c:309

From the < def normalized_columns_initializer(weights, std=1.0) > function
in line:

out *= std / torch.sqrt(out.pow(2).sum(1).expand_as(out)) # thanks to this initialization, we have var(out) = std^2

Best regards Søren Koch

You have two options:

  1. Run the code with PyTorch 0.1.12
  2. Change out *= std / torch.sqrt(out.pow(2).sum(1).expand_as(out)) to
    out *= std / torch.sqrt(out.pow(2).sum(1,keepdim=True).expand_as(out))

Wow okay i would never have got that one! Thanks Shani!

Jesus i feel like a noob asking one more question but i have a last problem… I sure your awesomeness can easily handle this…

Now i get:
ValueError: not enough values to unpack (expected 2, got 1)

From the function
in line:

inputs, (hx, cx) = state 

Best regards Søren Koch

No problem.
What do you send as a state? Your input should have 2 items, a state (in Atari it’s an image) and a tuple of the LSTM hidden and cell states (hx,cx) from the previous run.
Example from A3C:

        if done: # Initialization
            cx = Variable(torch.zeros(1, 256))
            hx = Variable(torch.zeros(1, 256))
        else: # The hx,cx from the previous iteration
            cx = Variable(cx.data)
            hx = Variable(hx.data)
value, logit, (hx, cx) = model(
                (Variable(state.unsqueeze(0)), (hx, cx)))  # notice what the model receives as inputs.

I only sent a state because i do not understand how or where i implement the hx and cx (Just for the record i am not using CNN im just feeding temperatures into my model). The state i am feeding can be seen in function and .

Here is the whole code

# AI for temperature regulator for pump

# Importing the libraries

import numpy as np
import random # random samples from different batches (experience replay)
import os # For loading and saving brain
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim # for using stochastic gradient descent
import torch.autograd as autograd # Conversion from tensor (advanced arrays) to avoid all that contains a gradient
# We want to put the tensor into a varaible taht will also contain a
# gradient and to this we need:
from torch.autograd import Variable
# to convert this tensor into a variable containing the tensor and the gradient

# Initializing and setting the variance of a tensor of weights
def normalized_columns_initializer(weights, std=1.0):
    out = torch.randn(weights.size())
    out *= std / torch.sqrt(out.pow(2).sum(1,keepdim=True).expand_as(out)) # thanks to this initialization, we have var(out) = std^2
    return out

# Initializing the weights of the neural network in an optimal way for the learning
def weights_init(m):
    classname = m.__class__.__name__ # python trick that will look for the type of connection in the object "m" (convolution or full connection)
    if classname.find('Linear') != -1:
        weight_shape = list(m.weight.data.size()) #?? list containing the shape of the weights in the object "m"
        fan_in = weight_shape[1] # dim1
        fan_out = weight_shape[0] # dim0
        w_bound = np.sqrt(6. / (fan_in + fan_out)) # weight bound
        m.weight.data.uniform_(-w_bound, w_bound) # generating some random weights of order inversely proportional to the size of the tensor of weights
        m.bias.data.fill_(0) # initializing all the bias with zeros

# Creating the architecture of the Neural Network
class Network(nn.Module): #inherinting from nn.Module
    
    #Self - refers to the object that will be created from this class
    #     - self here to specify that we're referring to the object
    def __init__(self, input_size, nb_action): #[self,input neuroner, output neuroner]
        super(Network, self).__init__() #inorder to use modules in torch.nn
        # Input and output neurons
        self.lstm = nn.LSTMCell(input_size, 30) # making an LSTM (Long Short Term Memory) to learn the temporal properties of the input
        self.fcL = nn.Linear(30, nb_action) # full connection of the
        self.apply(weights_init) # initilizing the weights of the model with random weights
        self.fcL.weight.data = normalized_columns_initializer(self.fcL.weight.data, 0.01) # setting the standard deviation of the fcL tensor of weights to 0.01
        self.fcL.bias.data.fill_(0) # initializing the actor bias with zeros
        self.lstm.bias_ih.data.fill_(0) # initializing the lstm bias with zeros
        self.lstm.bias_hh.data.fill_(0) # initializing the lstm bias with zeros
        self.train() # setting the module in "train" mode to activate the dropouts and batchnorms

		
		
    # For function that will activate neurons and perform forward propagation
    def forward(self, state):
        inputs, (hx, cx) = state # getting separately the input images to the tuple (hidden states, cell states)
        x = F.relu(self.lstm(inputs)) # forward propagating the signal from the input images to the 1st convolutional layer
        hx, cx = self.lstm(x, (hx, cx)) # the LSTM takes as input x and the old hidden & cell states and ouputs the new hidden & cell states
        x = hx # getting the useful output, which are the hidden states (principle of the LSTM)
        return self.fcL(x), (hx, cx) # returning the output of the actor (Q(S,A)), and the new hidden & cell states ((hx, cx))


# Implementing Experience Replay
# We know that RL is based on MDP
# So going from one state(s_t) to the next state(s_t+1)
# We gonna put 100 transition between state into what we call the memory
# So we can use the distribution of experience to make a decision
class ReplayMemory(object):
    
    def __init__(self, capacity):
        self.capacity = capacity #100 transitions
        self.memory = [] #memory to save transitions
    
    # pushing transitions into memory with append
    #event=transition
    def push(self, event):
        self.memory.append(event)
        if len(self.memory) > self.capacity: #memory only contain 100 events
            del self.memory[0] #delete first transition from memory if there is more that 100
    
    # taking random sample
    def sample(self, batch_size):
        #Creating variable that will contain the samples of memory
        #zip =reshape function if list = ((1,2,3),(4,5,6)) zip(*list)= (1,4),(2,5),(3,6)
        #                (state,action,reward),(state,action,reward)  
        samples = zip(*random.sample(self.memory, batch_size))
        #This is to be able to differentiate with respect to a tensor
        #and this will then contain the tensor and gradient
        #so for state,action and reward we will store the seperately into some
        #bytes which each one will get a gradient
        #so that eventually we'll be able to differentiate each one of them
        return map(lambda x: Variable(torch.cat(x, 0)), samples)

# Implementing Deep Q Learning

class Dqn():
    
    def __init__(self, input_size, nb_action, gamma, lrate, T):
        self.gamma = gamma #self.gamma gets assigned to input argument
        self.T = T
        # Sliding window of the evolving mean of the last 100 events/transitions
        self.reward_window = []
        #Creating network with network class
        self.model = Network(input_size, nb_action)
        #creating memory with memory class
        #We gonna take 100000 samples into memory and then we will sample from this memory to 
        #to get a snakk number of random transitions
        self.memory = ReplayMemory(100000)
        #creating optimizer (stochastic gradient descent)
        self.optimizer = optim.Adam(self.model.parameters(), lr = lrate) #learning rate
        #input vector which is batch of input observations
        #by unsqeeze we create a fake dimension to this is
        #what the network expect for its inputs
        #have to be the first dimension of the last_state
        self.last_state = torch.Tensor(input_size).unsqueeze(0)
        #Inilizing
        self.last_action = 0
        self.last_reward = 0
    
    def select_action(self, state):
        #Q value depends on state
        #Temperature parameter T will be a positive number and the closer
        #it is to ze the less sure the NN will when taking an action
        #forexample
        #softmax((1,2,3))={0.04,0.11,0.85} ==> softmax((1,2,3)*3)={0,0.02,0.98} 
        #to deactivate brain then set T=0, thereby it is full random
        probs = F.softmax((self.model(Variable(state, volatile = True))*self.T),dim=1) # T=100
        #create a random draw from the probability distribution created from softmax
        action = probs.multinomial()
        return action.data[0,0]
    
    # See section 5.3 in AI handbook
    def learn(self, batch_state, batch_next_state, batch_reward, batch_action):
        outputs = self.model(batch_state).gather(1, batch_action.unsqueeze(1)).squeeze(1)
        #next input for target see page 7 in attached AI handbook
        next_outputs = self.model(batch_next_state).detach().max(1)[0]
        target = self.gamma*next_outputs + batch_reward
        #Using hubble loss inorder to obtain loss
        td_loss = F.smooth_l1_loss(outputs, target)
        #using  lass loss/error to perform stochastic gradient descent and update weights 
        self.optimizer.zero_grad() #reintialize the optimizer at each iteration of the loop
        #This line of code that backward propagates the error into the NN
        #td_loss.backward(retain_variables = True) #userwarning
        td_loss.backward(retain_graph = True)
		#And this line of code uses the optimizer to update the weights
        self.optimizer.step()
    
    def update(self, reward, new_signal):
        #Updated one transition and we have dated the last element of the transition
        #which is the new state
        new_state = torch.Tensor(new_signal).float().unsqueeze(0)
        self.memory.push((self.last_state, new_state, torch.LongTensor([int(self.last_action)]), torch.Tensor([self.last_reward])))
        #After ending in a state its time to play a action
        action = self.select_action(new_state)
        if len(self.memory.memory) > 100:
            batch_state, batch_next_state, batch_action, batch_reward = self.memory.sample(100)
            self.learn(batch_state, batch_next_state, batch_reward, batch_action)
        self.last_action = action
        self.last_state = new_state
        self.last_reward = reward
        self.reward_window.append(reward)
        if len(self.reward_window) > 1000:
            del self.reward_window[0]
        return action
    
    def score(self):
        return sum(self.reward_window)/(len(self.reward_window)+1.)
    
    def save(self):
        torch.save({'state_dict': self.model.state_dict(),
                    'optimizer' : self.optimizer.state_dict(),
                   }, 'last_brain.pth')
    
    def load(self):
        if os.path.isfile('last_brain.pth'):
            print("=> loading checkpoint... ")
            checkpoint = torch.load('last_brain.pth')
            self.model.load_state_dict(checkpoint['state_dict'])
            self.optimizer.load_state_dict(checkpoint['optimizer'])
            print("done !")
        else:
            print("no checkpoint found...")

Thanks for you for you quick responses!

Best regards Søren Koch

Hello,
Did you figure out how to proceed?
My question is how is the (hx, cx) tuple will be updated when we optimise with a sampled batch from the buffer. Do we have to also store (hx,cx) along with state - action - new state - reward transitions?
I am asking this question to extend to DRQN from DQN implementation.
Thanks!

Hey Everyone !!
Can Anyone Please Explain me the meaning and source of this formulae
out *= std/torch.sqrt(out.pow(2).sum(1).expand_as(out))??
Best Regards,

From my point of view, you don’t have to save hidden states of each transition. Hidden states are computed based on input sequences fed to the recurrent layer.

However, it might be also possible to store hidden states along the transitions, but then you wouldn’t feed sequences and thus do not benefit from BPTT. I tried that approach for a PPO policy and workerd well for CartPole (masked velocity) and Minigrid-Hallway.