8 Best Free Resources To Learn Deep Reinforcement Learning Using TensorFlow

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With the success of DeepThoughts’s AlphaGo system defeating the world Go champion, reinforcement learning has achieved important consideration amongst researchers and builders. Deep reinforcement learning has turn into some of the important strategies in AI that can be being utilized by the researchers so as to attain synthetic normal intelligence. 

Below here’s a listing of 10 greatest free sources, in no explicit order to study deep reinforcement learning utilizing TensorFlow.

Introduction to RL and Deep Q Networks

About: This tutorial “Introduction to RL and Deep Q Networks” is supplied by the builders at TensorFlow. The matters embody 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 extra.

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A Free Course in Deep Reinforcement Learning from Beginner to Expert

About: This course is a collection of articles and movies the place you’ll grasp the abilities and architectures you want, to turn into a deep reinforcement learning skilled. Here, you’ll discover ways to implement brokers with Tensorflow and PyTorch that learns to play Space invaders, Minecraft, Starcraft, Sonic the Hedgehog and extra.

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Reinforcement Learning Tutorial with TensorFlow

About: In this tutorial, you can be launched with the broad ideas of Q-learning, which is a well-liked reinforcement studying paradigm. You will begin with an introduction to reinforcement studying, the Q-learning rule and in addition discover ways to implement deep Q studying in TensorFlow. 

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Tensorflow Reinforcement Learning: Introduction and Hands-On Tutorial

About: This article explains the basics of reinforcement studying, use Tensorflow’s libraries and extensions to create reinforcement studying fashions and strategies. You will discover ways to handle your Tensorflow experiments by MissingLink’s deep studying platform. The matters embody an introduction to deep reinforcement studying and its use-cases, reinforcement studying in Tensorflow, examples utilizing TF-Agents and extra.

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Deep Reinforcement Learning With TensorFlow 2.1

About: In this tutorial, you’ll perceive an outline of the TensorFlow 2.x options by the lens of deep reinforcement studying (DRL). You will likely be implementing a bonus actor-critic (A2C) agent in addition to resolve the basic CartPole-v0 surroundings. The matters embody (Asynchronous) Advantage Actor-Critic With TensorFlow 2.1, static computational graphs and extra. 

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See Also


Tensorflow Neural Networks Using Deep Q-Learning Techniques

About: In this course, you’ll discover ways to use OpenAI Gym for mannequin coaching, assemble and practice a Neural Network in Tensorflow utilizing Q-Learning strategies, enhance Q-Learning strategies with enhancements similar to Dueling Q and Prioritized Experience Replay (PER), and many others. By the top of this tutorial, you’ll discover ways to practice a reinforcement studying agent to play Atari video video games autonomously utilizing Deep Q-Learning with Tensorflow and OpenAI’s Gym API.

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Advanced Deep Learning & Reinforcement Learning

About: Advanced Deep Learning & Reinforcement Learning is a set of video tutorials on YouTube, supplied by DeepThoughts. Here, you’ll study machine learning-based AI, TensorFlow, neural community foundations, deep reinforcement studying brokers, basic video games examine and rather more.

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Deep Q Learning With Tensorflow 2

About: In this video tutorial, you’ll perceive code a Deep Q Learning agent utilizing TensorFlow 2 from scratch. You will discover ways to code a replay reminiscence in addition to to code up a sequential mannequin in Keras / TensorFlow 2. During the tutorial, additionally, you will study some fundamental rules of object-oriented python programming, and wrap it up with a evaluation of the agent’s efficiency.

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Ambika Choudhury

Ambika Choudhury

A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and studying one thing out of the field. Contact: ambika.choudhury@analyticsindiamag.com


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