Hello there,

I get the following from my “def forward” Function.

```
state, (hx, cx) = inputs
ValueError: too many values to unpack (expected 2)
```

after running my code for some time.

Code:

```
# AI 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
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, inputs):
state, (hx, cx) = inputs
hx, cx = self.lstm(state, (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)
q_values = self.fcL(x)
return q_values, (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, params):
self.gamma = params.gamma #self.gamma gets assigned to input argument
self.tau = params.tau
# Sliding window of the evolving mean of the last 100 events/transitions
self.reward_window = []
#Creating network with network class
self.model = Network(params.input_size, params.action_size)
#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 = params.lr) #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(params.input_size).unsqueeze(0)
#Inilizing
self.last_action = 0
self.last_reward = 0
def select_action(self, state):
#LSTM
initialise = True # Initialise to zero at first iteration
if initialise:
cx = Variable(torch.zeros(1, 30))
hx = Variable(torch.zeros(1, 30))
else: # The hx,cx from the previous iteration
cx = Variable(cx.data)
hx = Variable(hx.data)
initialise = False
print('c')
print(cx)
print('h')
print(hx)
q_values, (hx,cx) = self.model((Variable(state), (hx,cx)))
probs = F.softmax((q_values)*self.tau,dim=1)
#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
```

any suggestions?