With the success of DeepMind’s AlphaGo system defeating the world Go champion, reinforcement learning has achieved significant attention among researchers and developers. Deep reinforcement learning has become one of the most significant techniques in AI that is also being used by the researchers in order to attain artificial general intelligence.
Below here is a list of 10 best free resources, in no particular order to learn deep reinforcement learning using TensorFlow.
Introduction to RL and Deep Q Networks
About: This tutorial “Introduction to RL and Deep Q Networks” is provided by the developers at TensorFlow. The topics include an introduction to deep reinforcement learning, the Cartpole Environment, introduction to DQN agent, Q-learning, Deep Q-Learning, DQN on Cartpole in TF-Agents and more.
Know more here.
A Free Course in Deep Reinforcement Learning from Beginner to Expert
About: This course is a series of articles and videos where you’ll master the skills and architectures you need, to become a deep reinforcement learning expert. Here, you will learn how to implement agents with Tensorflow and PyTorch that learns to play Space invaders, Minecraft, Starcraft, Sonic the Hedgehog and more.
Know more here.
Reinforcement Learning Tutorial with TensorFlow
About: In this tutorial, you will be introduced with the broad concepts of Q-learning, which is a popular reinforcement learning paradigm. You will start with an introduction to reinforcement learning, the Q-learning rule and also learn how to implement deep Q learning in TensorFlow.
Know more here.
Tensorflow Reinforcement Learning: Introduction and Hands-On Tutorial
About: This article explains the fundamentals of reinforcement learning, how to use Tensorflow’s libraries and extensions to create reinforcement learning models and methods. You will learn how to manage your Tensorflow experiments through MissingLink’s deep learning platform. The topics include an introduction to deep reinforcement learning and its use-cases, reinforcement learning in Tensorflow, examples using TF-Agents and more.
Know more here.
Deep Reinforcement Learning With TensorFlow 2.1
About: In this tutorial, you will understand an overview of the TensorFlow 2.x features through the lens of deep reinforcement learning (DRL). You will be implementing an advantage actor-critic (A2C) agent as well as solve the classic CartPole-v0 environment. The topics include (Asynchronous) Advantage Actor-Critic With TensorFlow 2.1, static computational graphs and more.
Know more here.
Tensorflow Neural Networks Using Deep Q-Learning Techniques
About: In this course, you will learn how to use OpenAI Gym for model training, construct and train a Neural Network in Tensorflow using Q-Learning techniques, improve Q-Learning techniques with enhancements such as Dueling Q and Prioritized Experience Replay (PER), etc. By the end of this tutorial, you will learn how to train a reinforcement learning agent to play Atari video games autonomously using Deep Q-Learning with Tensorflow and OpenAI’s Gym API.
Know more here.
Advanced Deep Learning & Reinforcement Learning
About: Advanced Deep Learning & Reinforcement Learning is a set of video tutorials on YouTube, provided by DeepMind. Here, you will learn about machine learning-based AI, TensorFlow, neural network foundations, deep reinforcement learning agents, classic games study and much more.
Know more here.
Deep Q Learning With Tensorflow 2
About: In this video tutorial, you will understand how to code a Deep Q Learning agent using TensorFlow 2 from scratch. You will learn how to code a replay memory as well as to code up a sequential model in Keras / TensorFlow 2. During the tutorial, you will also learn some basic principles of object-oriented python programming, and wrap it up with a review of the agent’s performance.
Know more here.
