Udemy – Curiosity Driven Deep Reinforcement Learning 2021-10
Udemy – Curiosity Driven Deep Reinforcement Learning 2021-10 Downloadly IRSpace
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Curiosity Driven Deep Reinforcement Learning, If reinforcement learning is to serve as a viable path to artificial general intelligence, it must learn to cope with environments with sparse or totally absent rewards. Most real life systems provided rewards that only occur after many time steps, leaving the agent with little information to build a successful policy on. Curiosity based reinforcement learning solves this problem by giving the agent an innate sense of curiosity about its world, enabling it to explore and learn successful policies for navigating the world. In this advanced course on deep reinforcement learning, motivated students will learn how to implement cutting edge artificial intelligence research papers from scratch. This is a fast paced course for those that are experienced in coding up actor critic agents on their own. We’ll code up two papers in this course, using the popular PyTorch framework.
The first paper covers asynchronous methods for deep reinforcement learning; also known as the popular asynchronous advantage actor critic algorithm (A3C). Here students will discover a new framework for learning that doesn’t require a GPU. We will learn how to implement multithreading in Python and use that to train multiple actor critic agents in parallel. We will go beyond the basic implementation from the paper and implement a recent improvement to reinforcement learning known as generalized advantage estimation. We will test our agents in the Pong environment from the Open AI Gym’s Atari library, and achieve nearly world class performance in just a few hours.
What you’ll learn
- How to Code A3C Agents
- How to Do Parallel Processing in Python
- How to Implement Deep Reinforcement Learning Papers
- How to Code the Intrinsic Curiosity Module
Who this course is for
- This course is for advanced students of deep reinforcement learning
Specificatoin of Curiosity Driven Deep Reinforcement Learning
- Publisher : Udemy
- Teacher : Phil Tabor
- Language : English
- Level : Expert
- Number of Course : 25
- Duration : 3 hours and 45 minutes
Content on 2023-7
Requirements
- Experience in coding actor critic agents
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Subtitle : English
Quality: 720p
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File size
1.6 GB
