Download Udemy - Curiosity Driven Deep Reinforcement Learning torrent

Udemy - Curiosity Driven Deep Reinforcement Learning

Torrent:	Udemy - Curiosity Driven Deep Reinforcement Learning
Description:	Curiosity Driven Deep Reinforcement Learning MP4 \| Video: h264, 1280x720 \| Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning \| Language: English + srt \| Duration: 25 lectures (3h 45m) \| Size: 1.59 GB How Agents Can Learn In Environments With No Rewards 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 Requirements Experience in coding actor critic agents Description 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.
Downloads:	52
Category:	Other /Tutorials
Size:	1.9 GB
Show Files »	[ FreeCourseWeb.com ] Udemy - Curiosity Driven Deep Reinforcement Learning Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1. Introduction 1. What You Will Learn in this Course.mp4 (51.6 MB) 1. What You Will Learn in this Course.srt (8.5 KB) 2. How to Succeed in this Course.mp4 (33.5 MB) 2. How to Succeed in this Course.srt (6.0 KB) 2.1 Course github.html (0.1 KB) 3. Required Background, Software, and Hardware.mp4 (26.3 MB) 3. Required Background, Software, and Hardware.srt (4.9 KB) 2. Fundamental Concepts 1. A Brief Review of Deep Reinforcement Learning and Actor Critic Methods.mp4 (86.3 MB) 1. A Brief Review of Deep Reinforcement Learning and Actor Critic Methods.srt (13.7 KB) 2. Code Review of Basic Actor Critic Agent.mp4 (71.7 MB) 2. Code Review of Basic Actor Critic Agent.srt (13.6 KB) 3. A Crash Course in Asynchronous Advantage Actor Critic Methods.mp4 (46.8 MB) 3. A Crash Course in Asynchronous Advantage Actor Critic Methods.srt (8.2 KB) 4. Our Code Structure.mp4 (4.7 MB) 4. Our Code Structure.srt (2.4 KB) 3. Paper Analysis Asynchronous Methods for Deep Reinforcement Learning 1. How to Read and Implement Research Papers.mp4 (61.3 MB) 1. How to Read and Implement Research Papers.srt (10.8 KB) 1.1 A3C Paper.html (0.1 KB) 10. A3C Paper Experiments and Discussion.mp4 (101.3 MB) 10. A3C Paper Experiments and Discussion.srt (16.8 KB) 11. How to Modify the Open AI Gym Atari Environments.mp4 (163.3 MB) 11. How to Modify the Open AI Gym Atari Environments.srt (21.2 KB) 11.1 Open AI Gym Wrapper Documentation.html (0.1 KB) 12. Coding Our Main Loop and Evaluating Our Agent.mp4 (228.4 MB) 12. Coding Our Main Loop and Evaluating Our Agent.srt (28.4 KB) 2. A3C Paper Abstract and Introduction.mp4 (39.4 MB) 2. A3C Paper Abstract and Introduction.srt (6.6 KB) 3. Crash Course in Parallel Processing in Python.mp4 (84.3 MB) 3. Crash Course in Parallel Processing in Python.srt (14.0 KB) 4. A3C Paper Related Work, Reinforcement Learning Background.mp4 (19.3 MB) 4. A3C Paper Related Work, Reinforcement Learning Background.srt (8.3 KB) 5. A3C Paper The Asynchronous Reinforcement Learning Framework.mp4 (86.1 MB) 5. A3C Paper The Asynchronous Reinforcement Learning Framework.srt (12.7 KB) 6. Coding our Actor Critic Network.mp4 (96.7 MB) 6. Coding our Actor Critic Network.srt (12.6 KB) 7. Learning with Generalized Advantage Estimation.mp4 (114.8 MB) 7. Learning with Generalized Advantage Estimation.srt (16.0 KB) 8. Coding a Minimalist Replay Memory.mp4 (19.4 MB) 8. Coding a Minimalist Replay Memory.srt (3.7 KB) 9. Coding the Shared Adam Optimizer.mp4 (13.8 MB) 9. Coding the Shared Adam Optimizer.srt (2.8 KB) 9.1 Morvan Zhou's implementation of A3C.html (0.1 KB) 4. Paper Analysis Curiosity Driven Exploration by Self Supervised Prediction 1. Paper Overview.mp4 (9.5 MB) 1. Paper Overview.srt (2.1 KB) 1.1 The ICM Paper.html (0.1 KB) 2. ICM Paper Abstract and Introduction.mp4 (74.7 MB) 2. ICM Paper Abstract and Introduction.srt (11.5 KB) 3. ICM Paper Curiosity Driven Exploration.mp4 (79.1 MB) 3. ICM Paper Curiosity Driven Exploration.srt (12.7 KB) 4. Experimental Setup and Coding Our ICM Module.mp4 (232.7 MB) 4. Experimental Setup and Coding Our ICM Module.srt (28.4 KB) 5. ICM Paper Experiments, Related Work, and Discussion.mp4 (111.0 MB) 5. ICM Paper Experiments, Related Work, and Discussion.srt (19.2 KB) 6. Setting Up the Mini World and Training Our ICM Agent.mp4 (82.1 MB) 6. Setting Up the Mini World and Training Our ICM Agent.srt (9.4 KB) 6.1 The MiniWorld Github.html (0.1 KB) Bonus Resources.txt (0.3 KB) files
Added:	2021-10-10 11:07:21
Language:	English
Peers:	Seeders : 7 , Leechers : 11
Release name:	Udemy - Curiosity Driven Deep Reinforcement Learning
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