Cutting-Edge AI: Deep Reinforcement Learning in Python
Last Updated 10/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 50 lectures (8h 32m) | Size: 2 GB
Apply deep learning to artificial intelligence and reinforcement learning using evolution strategies, A2C, and DDPG
What you'll learn
Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines)
Understand and implement Evolution Strategies (ES) for AI
Understand and implement DDPG (Deep Deterministic Policy Gradient)
Requirements
Know the basics of MDPs (Markov Decision Processes) and Reinforcement Learning
Helpful to have seen my first two Reinforcement Learning courses
Know how to build a convolutional neural network in Tensorflow
Description
Welcome to Cutting-Edge AI!
This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course.
Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks).
While both of these have been around for quite some time, it's only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning.
The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer.
Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be.
We've seen how AlphaZero can master the game of Go using only self-play.
This is just a few years after the original AlphaGo already beat a world champion in Go.
We've seen real-world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation.
Simulation is...
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