Marc-Philippe HUGET @mphuget
This account reflects my current interests: #AI #RL, #multiagent, #deeplearning, #ReinforcementLearning France, the Alps Joined February 2011-
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We are pleased to announce that the 2020 @NeurIPSConf MineRL Competition has officially launched! This is an exciting collaboration between @mldcmu, @OpenAI, @PreferredNet, @aicrowdHQ, @MSFTResearch, and @DeepMind. Check it out and sign up here: minerl.io/competition
Slime Volleyball Gym Environment A simple gym environment for testing single and multi-agent reinforcement learning algorithms. Includes a few self-play training examples in python. github.com/hardmaru/slime…
Our paper (arxiv.org/abs/1905.01072) on deep residual reinforcement learning has been nominated for a Best Paper award at AAMAS!
Our paper (arxiv.org/abs/1905.01072) on deep residual reinforcement learning has been nominated for a Best Paper award at AAMAS!
OpenAI's multi-agent team is currently hiring research scientists! Interested in multiple, interacting agents, AI-generating algorithms, open-ended algorithms, automatically generating training environments, deep RL, and more? Please apply! openai.com/jobs
Want to learn about meta-learning? Lecture videos for CS330 are now online! youtube.com/playlist?list=… Topics incl. MTL, few-shot learning, Bayesian meta-learning, lifelong learning, meta-RL & more: cs330.stanford.edu + 3 guest lectures from Kate Rakelly, @svlevine, @jeffclune
We're standardizing OpenAI's deep learning framework on PyTorch to increase our research productivity at scale on GPUs (and have just released a PyTorch version of Spinning Up in Deep RL): openai.com/blog/openai-py…
Over 300% funded for the Neural Network from Scratch book @daniel_kukiela and I are writing! Over a hundred people in the draft doc Special shout out for all the people contributing to the doc too. Got some grammar experts shaping things up nicely :) <3 kickstarter.com/projects/sentd…
Deep RL agents are data hungry and often learn task-specific representations. Our model learns object-centric abstractions from raw videos. This enables highly data-efficient RL and structured exploration. arxiv.org/abs/1906.11883
We’re releasing a Neural MMO — a massively multiagent game environment that supports numerous populations of agents: blog.openai.com/neural-mmo/ - Code: github.com/openai/neural-… - 3D Client: github.com/jsuarez5341/ne…
Model-Based Reinforcement Learning for Atari They show that the simple, iterative method of learning a world model is enough to get ~SOTA data-efficient results on various Atari games. Only 100K interactions between agent and game (2hrs of realtime play) arxiv.org/abs/1903.00374
Evolving neural net controllers for a team of self-organizing robots. This 2010 work used self-play tournament selection to evolve a team of cooperative agents that plays a simulated soccer game. Interesting strategies emerged after only 500 generations. goo.gl/XvjJNN
Multi-Agent Soccer Environment from DeepMind Training agents to get the ball over to the other side is really my thing. GitHub: goo.gl/aBFoqM Paper: goo.gl/eHV4kz
One more step in the journey of making cooperative multi-agent RL reproducible 🧐. Considering the number of papers that use one-off toy environments in this field, this benchmark environment was long overdue..
One more step in the journey of making cooperative multi-agent RL reproducible 🧐. Considering the number of papers that use one-off toy environments in this field, this benchmark environment was long overdue..
Creating a Zoo of Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning. Great work led by Joel Lehman @joelbot3000, w/ many excellent collaborators from Uber AI Labs, OpenAI, & Google Brain. Blog: eng.uber.com/atari-zoo-deep… Code: github.com/uber-research/…

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Andrew Crooks @AndyCrooks
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Fortnite @Fortnite
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Maximilian Igl @MaxiIgl
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Daniel Situnayake @dansitu
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NeurIPS Conference @NeurIPSConf
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AIIDE StarCraft AI Co... @StarCraftAIComp
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