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5c5b473 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | import numpy as np
import random
import torch
import torch.nn as nn
import torch.optim as optim
# 🧠 Neural Network
class DQN(nn.Module):
def __init__(self, state_size, action_size):
super(DQN, self).__init__()
self.layers = nn.Sequential(
nn.Linear(state_size, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, action_size)
)
def forward(self, x):
return self.layers(x)
# 🤖 Agent
class DQNAgent:
def __init__(self, action_space, state_size):
self.action_space = action_space
self.state_size = state_size
self.epsilon = 1.0
self.epsilon_decay = 0.995
self.epsilon_min = 0.01
self.gamma = 0.95
self.lr = 0.001
self.memory = []
self.model = DQN(state_size, len(action_space))
self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
self.criterion = nn.MSELoss()
# 🎯 Action selection
def choose_action(self, state):
if random.random() < self.epsilon:
return random.choice(self.action_space)
state_tensor = torch.FloatTensor(state).unsqueeze(0)
q_values = self.model(state_tensor)
action_index = torch.argmax(q_values).item()
return self.action_space[action_index]
# 💾 Store experience
def remember(self, state, action, reward, next_state, done):
action_index = self.action_space.index(action)
self.memory.append((state, action_index, reward, next_state, done))
# 🧠 Learning step
def learn(self, batch_size=32):
if len(self.memory) < batch_size:
return
batch = random.sample(self.memory, batch_size)
for state, action, reward, next_state, done in batch:
state = torch.FloatTensor(state)
next_state = torch.FloatTensor(next_state) if next_state is not None else None
target = reward
if not done and next_state is not None:
target += self.gamma * torch.max(self.model(next_state)).item()
target_f = self.model(state)
target_f[action] = target
self.optimizer.zero_grad()
loss = self.criterion(self.model(state), target_f)
loss.backward()
self.optimizer.step()
# 🔻 Reduce randomness over time
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay |