Greg Farquhar

[Greg Farquhar @greg_far]

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.

[Angelos Filos @filangelos]

👽 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! 🧵

[Greg Farquhar @greg_far]

@pfau Member of Theological Staff?

[Greg Farquhar @greg_far]

This was a great project to work on. Happy to have it published now in @Nature! Meta-learning is important.

[Junhyuk Oh @junh_oh]

Excited to announce that our work on “Discovering state-of-the-art RL algorithms” is finally published in @Nature! In this work, we meta-learned RL algorithms at scale.

Paper: https://t.co/3V4TmPTWm4
Blog: https://t.co/G65ReK2iMs

See thread 👇

[Greg Farquhar @greg_far]

@j_foerst In case it's not where you got this, fyi https://t.co/NT8kAJVvlh

[Greg Farquhar @greg_far]

There are a bunch of ideas in this paper, but it all fits together really neatly! Great work from @filangelos and team 👏

[Greg Farquhar @greg_far]

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.

[Angelos Filos @filangelos]

👽 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! 🧵

[Greg Farquhar @greg_far]

@risi1979 Combining Deep Reinforcement Learning and Search for Imperfect-Information Games https://t.co/RB2ptFmwab from @polynoamial @anton_bakhtin et al. kinda has it all -- clarity, insights, theory, great empirical results, code available 👏

[Greg Farquhar @greg_far]

@NandoDF And if you want to have children while awaiting your ILR, no recourse for them that I'm aware of :(

[Greg Farquhar @greg_far]

@NandoDF Yes, acquiring ILR (settled status is I think a similar scheme for EU citizens) takes many years (can be 10 years in some cases) and is very expensive. I went through the absurd process (German citizen lived here 15 years) and it would be be very hard for less privileged folks.

[Greg Farquhar @greg_far]

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!

[Maximilian Igl @MaxiIgl]

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)

[Greg Farquhar @greg_far]

This is awesome, but I'm a little scared of how much time I might spend playing it myself...

[Tim Rocktäschel @_rockt]

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/

[Tim Rocktäschel @_rockt]

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/

[Greg Farquhar @greg_far]

I particularly enjoyed visualising & analysing the learned mixing functions that combine per-agent utilities into joint values!

[Mikayel Samvelyan @_samvelyan]

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

[Greg Farquhar @greg_far]

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 😀

[Greg Farquhar @greg_far]

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

[Noam Brown @polynoamial]

Tuomas Sandholm and I are doing a Reddit AMA now on the #Pluribus poker AI!

https://t.co/qOnCXFSJwe

[Greg Farquhar @greg_far]

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?? 😱

[Greg Farquhar @greg_far]

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

[Tim Rocktäschel @_rockt]

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:🤖+📖=📈🎯🏆🥳

[Tim Rocktäschel @_rockt]

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: https://t.co/hnqzC9zBWn