**Optimizing Battery Recycling with Reinforcement Learning**

Optimizing Battery Recycling with Reinforcement Learning

import numpy as np
env = gym.make('BatteryRecycling-v0')  # battery recycling environment
agent = DQN(env.observation_space, env.action_space)  # deep Q-network agent
env.reset()
reward = 0
for _ in range(10000):  # 10,000 recycling cycles
    action = agent.act(np.random.rand(env.observation_space.shape[0]))  # random exploration
    state, reward, done, _ = env.step(action)
    if done: 
        agent.update(target_net=env.model)
    else:
        agent.learn(state, reward)

This code snippet utilizes a Deep Q-Network (DQN) to optimize battery recycling within a simulated environment. The agent learns to maximize efficiency, minimizing waste and environmental impact while recovering valuable materials. Reinforcement learning enables the system to adapt and improve over time, leading to more sustainable battery recycling practices.

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