79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
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import os
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import torch
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from torch import nn, optim
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import torch.nn.functional as functional
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class LinearQNetwork(nn.Module):
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def __init__(self, input_size, hidden_size, output_size, pretrained=False):
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super().__init__()
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self.linear_one = nn.Linear(input_size, hidden_size)
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self.linear_two = nn.Linear(hidden_size, output_size)
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self.pretrained = pretrained
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def forward(self, x):
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x = functional.relu(self.linear_one(x))
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x = self.linear_two(x)
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return x
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def save(self, file_name='model.pth'):
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model_directory = 'model'
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if not os.path.exists(model_directory):
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os.makedirs(model_directory)
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file_path = os.path.join(model_directory, file_name)
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torch.save(self.state_dict(), file_path)
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@staticmethod
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def load(input_size, hidden_size, output_size, file_name='model.pth'):
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model_directory = 'model'
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file_path = os.path.join(model_directory, file_name)
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if os.path.isfile(file_path):
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model = LinearQNetwork(input_size, hidden_size, output_size, True)
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model.load_state_dict(torch.load(file_path))
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model.eval()
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return model
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return LinearQNetwork(11, 256, 3)
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class QTrainer:
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def __init__(self, model, lr, gamma):
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self.model = model
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self.lr = lr
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self.gamma = gamma
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self.optimizer = optim.Adam(model.parameters(), lr=self.lr)
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self.criterion = nn.MSELoss() # Mean squared error
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def train_step(self, state, action, reward, next_state, done):
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state = torch.tensor(state, dtype=torch.float)
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next_state = torch.tensor(next_state, dtype=torch.float)
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action = torch.tensor(action, dtype=torch.long)
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reward = torch.tensor(reward, dtype=torch.float)
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if len(state.shape) == 1:
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# reshape the state to make its values an (n, x) tuple
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state = torch.unsqueeze(state, 0)
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next_state = torch.unsqueeze(next_state, 0)
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action = torch.unsqueeze(action, 0)
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reward = torch.unsqueeze(reward, 0)
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done = (done,)
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# Prediction based on simplified Bellman's equation
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# Predict Q values for current state
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prediction = self.model(state)
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target = prediction.clone()
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for idx in range(len(done)):
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Q = reward[idx]
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if not done[idx]:
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Q = reward[idx] + self.gamma * torch.max(self.model(next_state[idx]))
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# set the target of the maximum value of the action to Q
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target[idx][torch.argmax(action).item()] = Q
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# Apply the loss function
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self.optimizer.zero_grad()
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loss = self.criterion(target, prediction)
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loss.backward()
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self.optimizer.step()
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