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Month: April 2019

Fundamental Concepts in Reinforcement Learning

Fundamental Concepts in Reinforcement Learning

We’ve touched on reinforcement learning many times here, as it represents our best chance at developing something approximating artificial general intelligence. We’ve covered everything from Monte Carlo methods, to Deep Q Learning, to Policy Gradient methods, using both the Pytorch and Tensorflow frameworks. What we haven’t discussed on this channel is the what and the how of reinforcement learning. That oversight ends today. Let’s get started. Essential concepts You’re probably familiar with supervised learning, which has been successfully applied to…

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Designing Your Own Open AI Gym Compatible Reinforcement Learning Environment

Designing Your Own Open AI Gym Compatible Reinforcement Learning Environment

The Open AI gym provides a wide variety of environments for testing reinforcement learning agents, however there will come a time when you need to design your own environment. Perhaps you are designing an inventory management system, or even creating an agent to perform real time bidding in search auctions. Whatever the use case, you will have to design your own environment, as there aren’t any off the shelf solutions that lend themselves to these tasks. It may seem a…

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