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In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine RLax with JAX, Haiku, and Optax to construct a Deep Q-Learning (DQN) agent that learns to solve the CartPole environment. Instead of using a fully packaged RL framework, we assemble the training pipeline ourselves so we can clearly understand how the core components of reinforcement learning interact. We define the neural network, build a replay buffer, compute temporal difference errors with RLax, and train the agent using gradient-based optimization. Also, we focus on understanding how RLax provides reusable RL primitives that can be integrated into custom reinforcement learning pipelines. We use JAX for efficient numerical computation, Haiku for neural network modeling, and Optax for optimization.
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随着低空经济的深入发展，其在沐川县的应用已超越最初的竹材运输范畴。在森林防护、应急救灾、巡检查勘、物资配送等多个领域，“低空+”的应用体系正在不断丰富和完善。
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