model.py

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import os

class Linear_QNet(nn.Module):
    """Define Linear_QNet

    Args:
        nn (Neural Network): Feed Foreward Neural network w/ 3 layers
        1. Input Layer = linear1
        2. Hidden Layer = linear2
        3. Output Layer = output
    """
    def __init__(self, input_size, hidden_size, output_size):

        super().__init__()
        self.linear1 = nn.Linear(input_size, hidden_size)
        self.linear2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        """Feed first function

        Returns:
            x - output: After the ReLU activation function; ReLU(x)=(x)+=max(0,x); is used
        """
        x = F.relu(self.linear1(x))
        x = self.linear2(x)
        return x

    def save(self, file_name='model.pth'):
        """Save data

        Args:
            file_name (): Records model at work - Defaults to 'model.pth'.
        """
        model_folder_path = './model'
        if not os.path.exists(model_folder_path):
            os.makedirs(model_folder_path)

        file_name = os.path.join(model_folder_path, file_name)
        torch.save(self.state_dict(), file_name)

class QTrainer:
    def __init__(self, model, lr, gamma):
        self.lr = lr #learning rate
        self.gamma = gamma #discount rate
        self.model = model
        self.optimizer = optim.Adam(model.parameters(), lr=self.lr)
        self.criterion = nn.MSELoss() #mean squared error

    def train_step(self, state, action, reward, next_state, done):
        """Pytorch training
        """
        state = torch.tensor(state, dtype=torch.float)
        next_state = torch.tensor(next_state, dtype=torch.float)
        action = torch.tensor(action, dtype=torch.long)
        reward = torch.tensor(reward, dtype=torch.float)
        #(n, x)

        if len(state.shape) == 1:
            #(1, x) num of batches
            state = torch.unsqueeze(state, 0)
            next_state = torch.unsqueeze(next_state, 0)
            action = torch.unsqueeze(action, 0)
            reward = torch.unsqueeze(reward, 0)
            done = (done, )

        #1: predicted Q values with current state
        pred = self.model(state)

        target = pred.clone()
        for idx in range(len(done)):
            Q_new = reward[idx]
            if not done[idx]:
                Q_new = reward[idx] + self.gamma * torch.max(self.model(next_state[idx]))

            target[idx][torch.argmax(action[idx]).item()] = Q_new
    
        #2: Q_new = r + y * max(next_predicted Q value) -> only do this if not done
        #pred.clone()
        #preds[argmax(action)] = Q_new
        self.optimizer.zero_grad()
        loss = self.criterion(target, pred)
        loss.backward()

        self.optimizer.step()