Sure, here is a brief introduction to reinforcement learning: Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with its environment. The agent receives feedback in the form of rewards or penalties for its actions, which helps it to learn the best strategies for achieving its goals. Unlike supervised learning, where the agent is given labeled data to learn from, reinforcement learning relies on trial and error to discover the most effective actions. This makes it particularly well-suited for tasks where there is no easily available training data or where the optimal strategy may not be known in advance. By continuously learning from its experiences, the agent can adapt to changing environments and improve its performance over time. 將此內容以HTML格式換行如下：
Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with its environment. The agent receives feedback in the form of rewards or penalties for its actions, which helps it to learn the best strategies for achieving its goals. Unlike supervised learning, where the agent is given labeled data to learn from, reinforcement learning relies on trial and error to discover the most effective actions. This makes it particularly well-suited for tasks where there is no easily available training data or where the optimal strategy may not be known in advance. By continuously learning from its experiences, the agent can adapt to changing environments and improve its performance over time.