在本篇文章中，我们全面而深入地探讨了强化学习（Reinforcement Learning）的基础概念、主流算法和实战步骤。 从马尔可夫决策过程（MDP）到高级算法如PPO，文章旨在为读者提供一 …

The goal of a reinforcement learning agent is to learn a policy: that maximizes the expected cumulative reward. Formulating the problem as a Markov decision process assumes the agent …

Dec 6, 2024 · View a PDF of the paper titled Reinforcement Learning: An Overview, by Kevin Murphy

Our focus is on reinforcement learning methods that involve learning while interacting with the environment, which evolutionary methods do not do (un- less they evolve learning algorithms, …

Nov 12, 2024 · 【万字长文】强化学习笔记 (Reinforcement Learning,RL)非常详细，初级入门

In reinforcement learning, an agent learns to make decisions by interacting with an environment. It is used in robotics and other decision-making settings.

强化学习 （英語： Reinforcement learning，簡稱 RL）是 机器学习 中的一个领域，强调如何基于 环境 而行动，以取得最大化的预期利益 [1]。

Reinforcement learning is learning what to do—how to map situations to actions——so as to maximize a numerical reward signal. ----- Richard S. Sutton and Andrew G. Barto …

Sep 15, 2025 · Reinforcement Learning (RL) is a branch of machine learning that focuses on how agents can learn to make decisions through trial and error to maximize cumulative rewards.

Reinforcement learning: RL, as we've explored, focuses on learning through interaction with an environment and receiving feedback in the form of rewards or penalties; it's like learning by...