Greg Farquhar
@greg_far
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Joined October 2017
There’s huge potential in using ‘demonstrations’ from other agents with different goals: to understand which features & dynamics of the environment *might* be important to you; and to borrow from others' behaviours only where they are useful for you.
👽 PsiPhi-learning 👽 (long talk #ICML) https://t.co/TA7gDtEHak shows how an agent can use data from the behavior of other agents with diverse goals: to infer their intentions and fulfill its own! 🧵
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There are a bunch of ideas in this paper, but it all fits together really neatly! Great work from @filangelos and team 👏
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Permanent damage to generalisation from early updates in non-stationary training -- really enjoyed looking into this intriguing problem and trying to solve it for deep RL agents!
Really excited about our new work: In deep RL, we typically collect new data using a non-stationary policy that gets updated as we learn and improve. We show this can impact the learning dynamics of our deep policy and lead to worse generalization https://t.co/1YTfpzDZOd (1/7)
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This is awesome, but I'm a little scared of how much time I might spend playing it myself...
I am proud to announce the release of the NetHack Learning Environment (NLE)! NetHack is an extremely difficult procedurally-generated grid-world dungeon-crawl game that strikes a great balance between complexity and speed for single-agent reinforcement learning research. 1/
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I am proud to announce the release of the NetHack Learning Environment (NLE)! NetHack is an extremely difficult procedurally-generated grid-world dungeon-crawl game that strikes a great balance between complexity and speed for single-agent reinforcement learning research. 1/
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I particularly enjoyed visualising & analysing the learned mixing functions that combine per-agent utilities into joint values!
Happy to share the extended version of our #QMIX paper “Monotonic Value Function Factorisation for Deep Multi-Agent RL” We include further analysis and ablation studies that investigate how monotonic factorisation of joint Q-val helps QMIX outperform VDN https://t.co/AGGADZgumu
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Potential for cool applications in meta-learning, multi-agent learning, etc. If you have ideas or want to chat, let me know or find me at NeurIPS 😀
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A much-improved 🎲Loaded DiCE🎲 objective lets you easily compute low-variance estimators of any-order derivatives for RL. Paper https://t.co/dllhrHuzwD and code https://t.co/NqZsdZy3iT online, nice working with @shimon8282 and @j_foerst! #NeurIPS2019
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Tuomas Sandholm and I are doing a Reddit AMA now on the #Pluribus poker AI! https://t.co/qOnCXFSJwe
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AI accelerates by 10x in the hour it takes to repost from r/machinelearning to r/singularityisnear... just how near is it at that rate?? 😱
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Progressively growing the action space creates a great curriculum for learning agents -- check out our paper: https://t.co/YoKe9ZIjhk + code: https://t.co/BdZjplNNEg. Great working with Laura Gustafson @ebetica @shimon8282 Nicolas Usunier @syhw
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How can RL agents exploit the compositional, relational and hierarchical structure of the world? A growing number of authors propose learning from natural language. We are excited to share our @IJCAIconf survey of this emerging field! https://t.co/XLHnXMQbVY TL;DR:🤖+📖=📈🎯🏆🥳
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I had the pleasure to co-supervise outstanding MSc students jointly with Jakob Foerster (@j_foerst) and Greg Farquhar (@greg_far) at @CompSciOxford this year. Together, we compiled our advice for embarking on short-term machine learning research projects:
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I am very excited to share our ICML paper “Deep Variational Reinforcement Learning (DVRL) for POMDPs”: Our agent learns a model of the environment and acts based on its belief state in this model. w/ @zinmalu @tuananhle7 @frankdonaldwood @shimon8282
https://t.co/XWh5QZ1saU
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Our latest paper: how to learn complex joint value functions for teams of agents whose greedy policies can be computed and executed in a decentralised fashion. The trick is a new monotonic value function factorisation. With results on StartCraft 2!
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The camera-ready of our #ICLR2018 paper “TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning” is now online https://t.co/sAUPHT91ho. Code is available at https://t.co/W5iRn8RLD4
@_rockt @MaxiIgl @shimon8282 @whi_rl
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Excited to share "DiCE: The Infinitely Differentiable Monte Carlo Estimator": https://t.co/LPEy67rCF0 Try this one weird objective for correct any-order gradient estimators in all your stochastic graphs ;) With fantastic Oxford/CMU team: @greg_far @alshedivat @_rockt @shimon8282
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