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所在平台: Udemy |
课程主页: https://www.udemy.com/course/reinforcement-learning-coursat/
课程评论：没有评论
Coursera 强化学习课程概述： 本课程是机器学习和人工智能领域一个令人兴奋且重要分支——强化学习（RL）的深入探讨。我们将从 RL 问题定义开始，将其与监督学习进行比较，并探索 RL 的应用领域。课程将覆盖从基础到深度学习的进阶应用，最终进入深度强化学习（DRL）时代。 我们将理论与实践相结合，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。 课程共分六个主要部分： 1. **RL 引言与问题定义**: 介绍 RL 基本概念，与监督学习对比，探索应用领域，并识别 RL 问题的关键组成部分。我们将重点介绍 OpenAI Gym 环境，为后续实践打下基础。 2. **马尔可夫决策过程 (MDP) 与动态规划**: 将 RL 问题形式化为 MDP，并用动态规划方法解决基础问题。 3. **模型无关方法**: 探索 MDP 的解决方案，重点是基于采样的方法，如蒙特卡洛和时间序列差分（TD）学习。我们将深入讲解 Q-learning 和 SARSA 算法，并在 OpenAI Gym 的表格型迷宫控制问题中进行实现。 4. **函数逼近与深度强化学习 (DRL)**: 引入函数逼近概念，过渡到基于深度学习的 DRL 方法。我们将重点介绍 DeepMind 的 DQN 算法，该算法成功解决了 Atari 游戏，并演示如何使用 Keras-RL 和 TF-Agents 在 Atari 游戏上实现 DQN。 5. **高级 DRL 算法**: 探讨基于策略的方法，包括 Policy Gradients、DDPG、Actor-Critic、A2C、A3C、TRPO 和 PPO。我们将使用 Stable Baselines 库在 diversos OpenAI Gym 环境中实现这些算法。 6. **模型相关方法与规划**: 探索模型相关 RL 方法，区分模型相关 RL 与规划，并全面审视 RL 方法的谱系。 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.
