Two Minute Papers has a video on a recently published paper about an AI that learns from observing humans and what makes this approach different than reinforcement learning.
Code Bullet has an interesting video on reinforcement learning/evolutionary AI to learn how to play Flappy Birds. As an added bonus, he also codes up his own Flappy Birds clone.
Lex Fridman interviews Pieter Abbeel, a professor at UC Berkeley and director of the Berkeley Robot Learning Lab. He is one of the top researchers in the world working on how to make robots understand and interact with the world around them, especially through imitation and deep reinforcement learning.
This interview is part of the Artificial Intelligence podcast and the MIT course 6.S099: Artificial General Intelligence. The conversation and lectures are free and open to everyone. Audio podcast version is available on https://lexfridman.com/ai/
In this video, Two Minute Papers explores the importance of curiosity to generalizable reinforcement learning algorithms.
The great AI Wizard Siraj Raval explains Move 37, reinforcement learning, and the future of human work in this video.
Code Bullet has video on how it learned how to play a hill racing game. Some foul language, Linkin Park soundbytes, and random shenanigans. Entertaining.
In case you didn’t know, I write a monthly column for MSDN Magazine on AI called “Artificially Intelligent”
In the last two articles, I covered one of the most exciting topics in AI in these days: reinforcement learning
Here’s a snippet and link to the full articles on MSDN.
In previous articles, I’ve mentioned both supervised learning and unsupervised learning algorithms. Beyond these two methods of machine learning lays another type: Reinforcement Learning (RL). Formally defined, RL is a computational approach to goal-oriented learning through interaction with the environment under ideal learning conditions.
Like other aspects of AI, many of the algorithms and approaches actively used today trace their origins back to the 1980s (bit.ly/2NZP177). With the advent of inexpensive storage and on-demand compute power, reinforcement learning techniques have re-emerged.
In last month’s column, I explored a few basic concepts of reinforcement learning (RL), trying both a strictly random approach to navigating a simple environment and then implementing a Q-Table to remember both past actions and which actions led to which rewards. In the demo, an agent working randomly was able to reach the goal state approximately 1 percent of the time and roughly half the time when using a Q-Table to remember previous actions. However, this experiment only scratched the surface of the promising and expanding field of RL.
In my session from Azure Data Fest Reston 2018, I explore reinforcement learning. As an added bonus, Andy (who’s holding the camera) chimes in now and then.
Press the play button below to listen here or visit the show page at DataDriven.tv