If you’re new to reinforcement learning (RL), there’s some great introductory courses out there. Just to name a few:
But if you’re anything like me, you might prefer a ‘learning by doing’ approach. With hands-on experience upfront, it may be easier for you to grasp the theory and math behind the algorithms later.
In this series, I’ll walk you through how to use Unity ML-Agents to build a volleyball environment and train agents to play in it using deep RL. For a…
Inspired by Slime Volleyball Gym, I built a 3D Volleyball environment for training reinforcement learning agents using Unity’s ML-Agents toolkit. The full project is open-source and available at: 🏐 Ultimate Volleyball.
In this article, I share an overview of the implementation details, challenges, and learnings from designing the environment to training an agent in it. For a background on ML-Agents, please check out my Introduction to ML-Agents article.
Versions used: Release 18 (June 9, 2021)
Python package: 0.27.0
Unity package: 2.1.0
Having no previous experience with game design or 3D modeling, I found Unity’s wide library of free assets and…
In March 2016, DeepMind’s AlphaGo beat Lee Sedol 4–1 in a televised match viewed by over 200 million people. There was a global shortage in Go boards, and AlphaGo’s victory is seen as a landmark moment for artificial intelligence.
Shortly after in April, OpenAI launched its Gym toolkit to help researchers develop and benchmark reinforcement learning algorithms. Reinforcement learning was a key technique used in the training of AlphaGo, along with deep learning.
Today, interest in artificial intelligence and machine learning is at an all-time high.
AI game competitions are also known as AI programming competitions or bot programming competitions. They’re different from your average data science competition. In an AI game competition, you aren’t given a data set. Instead, you get a game or simulation and your job is to program an agent that can compete in it (sometimes head-to-head against other players’ agents).
They can be a great place to practice programming, algorithms, and AI/ML. The competitions vary widely in their difficulty, prizes, languages available, and feasible strategies. …
This article is part 3 of the series ‘A hands-on introduction to deep reinforcement learning using Unity ML-Agents’. It’s also suitable for anyone new to Unity interested in using ML-Agents for their own reinforcement learning project.
In part 2, we built a 3D physics-based volleyball environment in Unity. …
This article is part 2 of the series ‘A hands-on introduction to deep reinforcement learning using Unity ML-Agents’. It’s also suitable for anyone new to Unity interested in using ML-Agents for their own reinforcement learning project.
Part 1: Getting started with Unity ML-Agents
Part 2: Building a volleyball reinforcement learning environment (this post)
Part 3: Design reinforcement learning agents using Unity ML-Agents
Part 4: Training an agent using PPO (Coming soon)
In my previous post, I went over how to set up ML-Agents and train an agent.
In this article, I’ll walk through how to build a 3D physics-based volleyball…
Whether you’re a beginner or veteran in machine learning and data science, you might be interested in a place to ask questions, share projects, or join discussions on the latest developments.
There are many great communities out there for this, but it can be difficult to choose which one (and some may no longer be active or well-maintained).
To help you, I’ve compiled an up-to-date list of 20+ active machine learning and data science communities grouped by platform.
Reddit is a powerhouse for many active forums dedicated to all areas across AI, machine learning, and data science.
Here’s a list:
In this tutorial, you’ll learn how to get set up with Unity’s ML-Agents toolkit and train your own agent using reinforcement learning. No previous experience with Unity will be needed.
ML-Agents is an add-on for the existing Unity platform. It provides researchers and game developers with the ability to build complex 3D environments and train intelligent agents in them — all while leveraging the powerful Unity engine and UI.
The A* pathfinding (or A* search) algorithm is a popular technique for finding the shortest path between two points in an environment.
In this tutorial, we’ll implement an A* pathfinding algorithm in Python to help our agent navigate to a target destination in the Dungeons and Data Structures environment. Make sure you check out Part 1 to get set up (and optionally Part 2 to create a simple bot).
Make sure you check out part 1 of this bot programming tutorial series first. So far we’ve covered:
In part 2, we’ll show you how to build a simple agent called the ‘Wanderer Agent’ that implements some scripted logic to explore and navigate the world. More specifically, it: