edureka! covers the basics of reinforcement learning in this tutorial for beginners.
Samuel Arzt shows off a project where an AI learns to park a car in a parking lot in a 3D physics simulation.
The simulation was implemented using Unity’s ML-Agents framework (https://unity3d.com/machine-learning).
From the video description:
The AI consists of a deep Neural Network with 3 hidden layers of 128 neurons each. It is trained with the Proximal Policy Optimization (PPO) algorithm, which is a Reinforcement Learning approach.
Microsoft Research jsut posted this talk from Reinforcement Learning Day 2019: “Towards Using Batch Reinforcement Learning to Identify Treatment Options in Healthcare.”
Dani, a game developer, recently made a game and decided to train an AI to play it.
A couple of weeks ago I made a video “Making a Game in ONE Day (12 Hours)”, and today I’m trying to teach an A.I to play my game!
Basically I’m gonna use Neural Networks to make the A.I learn to play my game.
This is something I’ve always wanted to do, and I’m really happy I finally got around to do it. Some of the biggest inspirations for this is obviously carykh, Jabrils & Codebullet!
In this DataPoint, Frank runs into Matt Kirk on the expo floor at Strata NYC. ress the play button below to listen here or visit the show page at DataDriven.tv
OpenAI has an interesting video explaining some of their latest research behind training reinforcement learning agents how to play hide and seek.
Two Minute Papers explores the paper “MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies” in this video.
This video picks up on the continuing project that DeepLizard has been working on to build a deep Q-network to master the cart and pole problem. Learn how to manage the environment and process images that will be passed to the deep Q-network as input.
TensorFlow developers interested in Reinforcement Learning (RL) may want to take a look at Huskarl. The framework was recently introduced in a Medium blog post and is meant for easy prototyping with deep-RL algorithms.
According to its creator, software engineer Daniel Salvadori, Huskarl “abstracts away the agent-environment interaction” in a similar way “to how TensorFlow abstracts away the management of computational graphs”. Under the hood it makes use of TensorFlow 2.0, naturally, and the tf.keras API. It is also implemented in a way that facilitates the parallelisation of computation of environment dynamics across CPU cores, to help in scenarios benefitting from multiple sources.