These representations are a powerful basis for navigating the world. Reinforcement learning agents equipped with them can quickly adapt their behaviour to visually novel environments.pic.twitter.com/4Sv3amP5Om
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These representations are a powerful basis for navigating the world. Reinforcement learning agents equipped with them can quickly adapt their behaviour to visually novel environments.pic.twitter.com/4Sv3amP5Om
Work by @benigno_uria, Borja Ibarz, @AndreaBanino, @fzvinicius, Dharshan Kumaran, @demishassabis, @caswellcaswell, and @BlundellCharles
@Numenta as well.
They are playing with sparsity here too.
Thanks - looks very interesting. You should cite the seminal work on RatSLAM by Milford et al. http://ieeexplore.ieee.org/document/1307183/ … and http://journals.sagepub.com/doi/10.1177/0278364909340592 … Also:https://pubmed.ncbi.nlm.nih.gov/25079451/
Representations, derived from a simple egocentric predictive framework, form an efficient basis-set for cognitive mapping. i. In the mammalian brain, allocentric rep. support efficient self-location & flexible navigation ii. SMP, model egocentric to allocentric understanding 1/3
A) Agent w/ SMP -Visual inputs, 3 visually different training env. stored in sep memory slot banks -SMP (light blue) trained, unsupervised expt to min. loss in prediction of memory reactivations -Policy (purple) trained to min. a comb. of policy grad, value & entropy losses 2/3pic.twitter.com/Z0iCY2xfZn
I've just been watching the video on your website again. It is still giving me goosebumps : ). Congratulations on solving the problem of how proteins fold and your CASP 14 achievement!! You guys and gals rock.
'almost there, fellows!
Wow that's great 

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