强化学习新手指导-30 天从不会到还是不会
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A practical deep learning guide in Chinese.
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Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties for its actions. This article provides an introductory overview of reinforcement learning and explains the basic concepts involved, including policy, reward function, and value function.
There are several popular algorithms used in reinforcement learning, including Q-learning, SARSA, and actor-critic methods. This article explains each of these algorithms in detail, including their strengths and weaknesses, and provides examples of how they can be applied to different problems.
If you're new to reinforcement learning, this article provides a step-by-step guide to getting started, including setting up your environment, choosing a problem to solve, and selecting an appropriate algorithm. It also includes tips for debugging and improving your model, as well as resources for further learning.