From f6caa84823ae9fb7b99e0193731a95fbdb85d9ff Mon Sep 17 00:00:00 2001 From: Sudharsan Ravichandiran Date: Thu, 29 Nov 2018 15:56:40 +0530 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c861df3..d13931a 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep learning, covering various deep learning algorithms. You will then explore deep reinforcement learning in depth, which is a combination of deep learning and reinforcement learning. You will master various deep reinforcement learning algorithms such as DQN, Double DQN. Dueling DQN, DRQN, A3C, DDPG, TRPO, and PPO. You will also learn about recent advancements in reinforcement learning such as imagination augmented agents, learn from human preference, DQfD, HER and many more. - +[For effective reading and better rendering, check all the notebooks here](http://nbviewer.jupyter.org/github/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/) ### [1. Introduction to Reinforcement Learning](https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python/tree/master/01.%20Introduction%20to%20Reinforcement%20Learning)