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所在平台: Udemy |
课程主页: https://www.udemy.com/course/reinforcement-learning-english/
课程评论：没有评论
**课程名称：** 强化学习 (Reinforcement Learning) - 精通RL的艺术 **课程概述：** 本课程将深入探讨机器学习和人工智能领域一个令人兴奋且至关重要的分支——强化学习（RL）。我们将从定义RL问题入手，将其与监督学习进行比较，并探索RL的广泛应用领域。课程将涵盖问题构建的各个方面，从基础概念到深度学习的先进应用，最终迈入深度强化学习（DRL）的时代。 我们将结合理论与实践，学习如何实现RL算法，并使用OpenAI Gym、Keras-RL、TensorFlow Agents (TF-Agents) 和 Stable Baselines 等库将其应用于经典问题。 **课程结构（6个主要部分）：** 1. **RL问题介绍：** 定义RL，与监督学习对比，介绍应用领域和RL问题的构成要素。重点介绍OpenAI Gym环境。 2. **马尔可夫决策过程 (MDP)：** 将RL问题形式化为MDP，并使用动态规划解决基本问题。 3. **模型无关方法：** 探索除动态规划以外的解决方案，重点关注模型无关方法（如蒙特卡洛和时序差分学习），包括Q-learning和SARSA算法。将在OpenAI Gym的表格型迷宫问题中演示其实际应用。 4. **深度强化学习 (DRL)：** 学习函数逼近在RL中的作用，介绍基于深度学习的RL方法。深入讲解DeepMind在Atari游戏和AlphaGo中应用的Deep Q-Networks (DQN)，并演示使用Keras-RL和TF-Agents在Atari游戏问题中实现DQN。 5. **高级DRL算法：** 涵盖基于策略的方法，如Policy Gradients、DDPG、Actor-Critic、A2C、A3C、TRPO和PPO。介绍Stable Baselines库，用于在OpenAI Gym的各种环境中实现这些算法。 6. **模型相关方法：** 探讨模型相关RL方法，区分模型相关RL与规划，并全面回顾RL方法。 本课程旨在帮助您全面掌握强化学习的理论知识和实践技能。
Hello and welcome to our course; Reinforcement Learning. Reinforcement Learning is a very exciting and important field of Machine Learning and AI. Some call it the crown jewel of AI.In this course, we will cover all the aspects related to Reinforcement Learning or RL. We will start by defining the RL problem, and compare it to the Supervised Learning problem, and discover the areas of applications where RL can excel. This includes the problem formulation, starting from the very basics to the advanced usage of Deep Learning, leading to the era of Deep Reinforcement Learning.In our journey, we will cover, as usual, both the theoretical and practical aspects, where we will learn how to implement the RL algorithms and apply them to the famous problems using libraries like OpenAI Gym, Keras-RL, TensorFlow Agents or TF-Agents and Stable Baselines.The course is divided into 6 main sections:1- We start with an introduction to the RL problem definition, mainly comparing it to the Supervised learning problem, and discovering the application domains and the main constituents of an RL problem. We describe here the famous OpenAI Gym environments, which will be our playground when it comes to practical implementation of the algorithms that we learn about.2- In the second part we discuss the main formulation of an RL problem as a Markov Decision Process or MDP, with simple solution to the most basic problems using Dynamic Programming.3- After being armed with an understanding of MDP, we move on to explore the solution space of the MDP problem, and what the different solutions beyond DP, which includes model-based and model-free solutions. We will focus in this part on model-free solutions, and defer model-based solutions to the last part. In this part, we describe the Monte-Carlo and Temporal-Difference sampling based methods, including the famous and important Q-learning algorithm, and SARSA. We will describe the practical usage and implementation of Q-learning and SARSA on control tabular maze problems from OpenAI Gym environments.4- To move beyond simple tabular problems, we will need to learn about function approximation in RL, which leads to the mainstream RL methods today using Deep Learning, or Deep Reinforcement Learning (DRL). We will describe here the breakthrough algorithm of DeepMind that solved the Atari games and AlphaGO, which is Deep Q-Networks or DQN. We also discuss how we can solve Atari games problems using DQN in practice using Keras-RL and TF-Agents.5- In the fifth part, we move to Advanced DRL algorithms, mainly under a family called Policy based methods. We discuss here Policy Gradients, DDPG, Actor-Critic, A2C, A3C, TRPO and PPO methods. We also discuss the important Stable Baseline library to implement all those algorithms on different environments in OpenAI Gym, like Atari and others.6- Finally, we explore the model-based family of RL methods, and importantly, differentiating model-based RL from planning, and exploring the whole spectrum of RL methods.Hopefully, you enjoy this course, and find it useful.
