Imagination-Augmented Agents for Deep Reinforcement Learning · HackerPapers discussions

Imagination-Augmented Agents for Deep Reinforcement Learning — mentioned by hardmaru

Tue, 17 Apr 2018 21:54:38 GMT

Hi, thanks for the feedback! Honestly we didn't intend to over-hype the results. We took the terms from existing works that we knew: 1) Alex Graves on Hallucination with Recurrent Neural Networks, a 2015 lecture at the University of Oxford from a course by Nando de Freitas (highly recommended). http://www.creativeai.net/posts/kp4bTG993JTQcqy2d/alex-grave... 2) Generating Sequences With Recurrent Neural Networks https://arxiv.org/abs/1308.0850 "Assuming the predictions are probabilistic, novel sequences can be generated from a trained network by iteratively sampling from the network’s output…

Paper and discussion history. A mention is not an endorsement.

Imagination-Augmented Agents for Deep Reinforcement Learning — HN submission

Thu, 10 Aug 2017 01:36:41 GMT

Imagination-Augmented Agents for Deep Reinforcement Learning

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Imagination-Augmented Agents for Deep Reinforcement Learning — mentioned by gwern

Wed, 09 Aug 2017 19:04:42 GMT

I think it is doable in under 5 years, but this critically depends on the resources invested by DM and other DL orgs. Deep RL is hugely demanding of computational resources to iterate your designs - for example, the first AlphaGo took something like 3 GPU-years to train it once (2 or 3 months parallelized); however, with much more iteration, DM was able to get Master's from-scratch training down to under 1 month. Now an AG researcher can iterate rapidly with small-scale hobbyist or researcher resources, but if they had had to do it all themselves, Ke Jie would still be waiting for a worthy…

Paper and discussion history. A mention is not an endorsement.