CPS.py

# Basic Template generated by x0prc. Modify according to usage.
import numpy as np
import random
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

# Define parameters and constants
EPISODES = 1000  
MAX_STEPS = 100  
GAMMA = 0.95  
EPSILON = 1.0  
EPSILON_MIN = 0.01  
EPSILON_DECAY = 0.995 
LEARNING_RATE = 0.001  

class DRLAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=2000)  # Replay memory
        self.gamma = GAMMA  # Discount rate

        # Exploration-exploitation parameters
        self.epsilon = EPSILON
        self.epsilon_min = EPSILON_MIN
        self.epsilon_decay = EPSILON_DECAY

        # Neural network model
        self.model = self._build_model()

    def _build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=LEARNING_RATE))
        return model

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])

    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma *
                          np.amax(self.model.predict(next_state)[0]))
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay


# Main function for training
def train_drl_agent():
    # Initialize environment and agent
    state_size = # Define state size based on environment
    action_size = # Define action size based on environment
    agent = DRLAgent(state_size, action_size)

    # Iterate over episodes
    for e in range(EPISODES):
        state = # Reset environment and get initial state
        for step in range(MAX_STEPS):
            # Choose action
            action = agent.act(state)
            # Perform action and observe reward and next state
            next_state, reward, done, _ = # Implement environment step function
            # Store experience in replay memory
            agent.remember(state, action, reward, next_state, done)
            # Update state
            state = next_state
            # Perform experience replay
            if len(agent.memory) > batch_size:
                agent.replay(batch_size)
            if done:
                break
        # Decay exploration rate after each episode
        if agent.epsilon > agent.epsilon_min:
            agent.epsilon *= agent.epsilon_decay


if __name__ == "__main__":
    train_drl_agent()