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20152026
most citedInfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

1.2k citations · 3.6k across the 11 of their papers we have counts for

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Showing 2018Show all

2 papers · 1 filter

cs.AI2018★ 66 cited

Some Considerations on Learning to Explore via Meta-Reinforcement Learning

Bradly C. Stadie, Ge Yang, Rein Houthooft +5

We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and E-. Results are present…

cs.LG2018★ 133 cited

Evolved Policy Gradients

Rein Houthooft, Richard Y. Chen, Phillip Isola +4

We propose a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms. The idea is to evolve a differentiable loss function, such that an agent, whi…