Объем поставок российской рыбы в ЕС увеличился15:01
Никита Хромин (ночной линейный редактор)
Michael Wooldridge. An introduction to multiagent systems. John wiley \& sons, 2009.。whatsapp网页版对此有专业解读
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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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