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Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

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Oct 30 2019 hardmaru 😷

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction Rather than hardcoding forward prediction, we try to get agents to *learn* that they need to predict the future. Check out our #NeurIPS2019 paper! https://learningtopredict.github.io https://arxiv.org/abs/1910.13038 https://t.co/XMjNyeRBK2
15 replies, 1224 likes


Oct 30 2019 Emtiyaz Khan

This is really amazing. I always say that “missing values” in reality are not always a problem but they can also be features. Perhaps we learn to predict things better when we know observations will be mostly missing. Congratulations @hardmaru and coauthors!
2 replies, 121 likes


Nov 14 2019 hardmaru

Matthew Crosby made a set of notes about our “Learning to Predict” paper for their reading group: https://t.co/J9loE2U7S7
2 replies, 87 likes


Oct 30 2019 Julian Togelius

Cool stuff. I strongly believe we need to move towards learning forward models in order to learn good policies, as model-free learning is so limited. But don't believe me; in this case, believe the networks, who learned by themselves that it was a good idea to learn a model.
1 replies, 85 likes


Nov 03 2019 ALife Papers

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction by C. D. Freeman, L. Metz and @hardmaru "forward-predictive modeling can arise as a side-effect of optimization under the right circumstances" https://learningtopredict.github.io/ https://arxiv.org/abs/1910.13038 https://t.co/JfdxAYp8MY
0 replies, 76 likes


Oct 30 2019 bucket of kets

This fantastic collaboration with @Luke_Metz, @hardmaru, and myself has been in the works for quite some time. Delighted to see it released! Enjoy David’s beautiful videos!
1 replies, 61 likes


Oct 30 2019 hardmaru 😷

@bucketofkets @Luke_Metz @NeurIPSConf We examine the role of inductive biases in the world model, and show that the architecture of the model plays a role in not only in performance, but also interpretability. Our paper will be presented at #NeurIPS2019: article https://learningtopredict.github.io/ arxiv https://arxiv.org/abs/1910.13038 https://t.co/xdGvAU7VRe
3 replies, 49 likes


Nov 05 2019 Tim

I love this paper by Daniel Freeman, @Luke_Metz, & @hardmaru. Simple, but clever & inspiring idea (rather then recently popular "throw more GPUs"). Plus, clearly written, with great visuals 👏
1 replies, 32 likes


Oct 30 2019 Douglas Eck

The cartpole world model animation you see here wonderfully sets the context for the paper. Great work!
0 replies, 22 likes


Oct 30 2019 Brandon Rohrer

World model building as a means to an end. "by identifying the important part of the world, policies could be trained significantly more quickly, or more sample efficiently"
1 replies, 11 likes


Oct 30 2019 roadrunner01

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction pdf: https://arxiv.org/pdf/1910.13038.pdf abs: https://arxiv.org/abs/1910.13038 webpage: https://learningtopredict.github.io/ https://t.co/tpBAiZaSkD
0 replies, 10 likes


Oct 30 2019 Moritz Schneider

New paper from @hardmaru . I love how well his papers and the related blog posts are made. Looking forward to read this!
2 replies, 7 likes


Oct 30 2019 Marek Bernát

This is a fascinating lesson in 'less is more'.
0 replies, 3 likes


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