Learning to reinforcement learn · HackerPapers discussions

Learning to reinforcement learn — mentioned by jimfleming

Thu, 28 Mar 2019 21:24:45 GMT

Hyperparameter optimization (including architectures) is not really meta-learning. Meta-learning, also known as "learning to learn", is more like MAML[0], RL2[1], L2RL[2], etc. 0. https://arxiv.org/abs/1703.03400 1. https://arxiv.org/abs/1611.02779 2. https://arxiv.org/abs/1611.05763

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Learning to reinforcement learn — mentioned by trextrex

Sat, 02 Jun 2018 22:47:19 GMT

Many concerns in this paper, especially about deep learning being data hungry, limited capacity for transfer, and integrating prior knowledge are addressed by recent papers in meta-learning and learning-to-learn [1,2,3 and many many others] both in the supervised and reinforcement learning contexts. In the case of meta-reinforcement learning there has been recent work [5] which seems to indicate that this mechanism is very similar to how learning works in the brain. In fact, my group recently published a paper [4] about learning-to-learn in spiking networks (making it biologically more…

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Learning to reinforcement learn — mentioned by willwhitney

Thu, 23 Feb 2017 00:42:01 GMT

Handling delays (and the uncertainty they entail) is a huge challenge, and I think it'll be a rich area of research. The simplest part of the problem is that delays in action or perception also slow the propagation of reward signals, and credit assignment is still a really hard problem. Thinking further afield, future models could learn to adapt their expectations to fit the behavior of a particular opponent. This kind of metalearning is pretty much a wide open problem, though a pair of (roughly equivalent) papers in this direction recently came out from DeepMind…

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Learning to reinforcement learn — mentioned by spacehacker

Tue, 03 Jan 2017 13:52:55 GMT

Another one: 'Learning to reinforcement learn' https://arxiv.org/abs/1611.05763

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Learning to reinforcement learn — mentioned by sapphireblue

Thu, 22 Dec 2016 01:05:55 GMT

Meta- reinforcement learning could prove to be such breakthrough, see [1],[2]. Also next generation ASIC accelerators (Google's TPU, Nervana) can give 10x increase in NN performance over a GPU manufactured on the same process, with another 10x possible with some form of binarized weights, e.g. BNN, XNOR-net. There are also interesting techniques to update the model's parameters in a sparse manner. So, there certainly is a lot of room left for performance improvements! 1. https://arxiv.org/abs/1611.05763 2. https://arxiv.org/abs/1611.02779

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Learning to reinforcement learn — mentioned by s4chin

Wed, 07 Dec 2016 19:17:44 GMT

Actually, googling 'learning to reinforcement learn' links to this paper - https://arxiv.org/abs/1611.05763

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Learning to reinforcement learn — mentioned by sapphireblue

Mon, 05 Dec 2016 14:38:56 GMT

Looks like the "UNREAL" (https://arxiv.org/abs/1611.05397), "Learning to reinforcement learn" (https://arxiv.org/abs/1611.05763) and "RL^2" (https://arxiv.org/abs/1611.02779) are state of art in pure RL for now. Finally there is a trend of using recurrent neural network as a top component of the Q-network. Perhaps we will see even more sophisticated RNNs like DNC and Recurrent Entity Networks applied here. Also we'll see meta-reinforcement learning applied to a curriculum of environments.

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Learning to reinforcement learn — HN submission

Sat, 19 Nov 2016 06:00:13 GMT

Learning to reinforcement learn

97 points; 9 discussion replies on HN. Paper and source history. A submission is not an endorsement.