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Слава отве

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Comprehens

In conclusion, we built a complete Deep Q-Learning agent by combining RLax with the modern JAX-based machine learning ecosystem. We designed a neural network to estimate action values, implement experience replay to stabilize learning, and compute TD errors using RLax’s Q-learning primitive. During training, we updated the network parameters using gradient-based optimization and periodically evaluated the agent to track performance improvements. Also, we saw how RLax enables a modular approach to reinforcement learning by providing reusable algorithmic components rather than full algorithms. This flexibility allows us to easily experiment with different architectures, learning rules, and optimization strategies. By extending this foundation, we can build more advanced agents, such as Double DQN, distributional reinforcement learning models, and actor–critic methods, using the same RLax primitives.

关键词:Слава отвеComprehens

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关于作者

李娜,资深行业分析师,长期关注行业前沿动态,擅长深度报道与趋势研判。

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