Gen Endo

Reinforcement Learning · 2004–2008

A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

Whom Gen Endo trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Kenji Doya
  2. Hajime Kimura
  3. Stefan Schaal
  4. Vijay R. Konda
  5. Jan Peters
  6. Jonathan Baxter
  7. Shigenobu Kobayashi
  8. Leslie Pack Kaelbling
  9. Auke Jan Ijspeert
  10. John N. Tsitsiklis

Who trusts Gen Endo overall

  1. Sergey Levine
  2. Jan Peters
  3. Chelsea Finn
  4. Pieter Abbeel
  5. Stefan Schaal
  6. Jens Kober
  7. Shixiang Shane Gu
  8. Xiaodan Liang
  9. Tianhao Zhang
  10. Trevor Darrell

Papers from Gen Endo's perspective

Ordered by how strongly the researchers Gen Endo trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Actor-Critic Algorithms
  2. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
  3. Policy Gradient Methods for Reinforcement Learning with Function Approximation
  4. Infinite-Horizon Policy-Gradient Estimation
  5. Reinforcement Learning for Humanoid Robotics
  6. Planning and Acting in Partially Observable Stochastic Domains
  7. Reinforcement Learning: An Introduction
  8. Stochastic policy gradient reinforcement learning on a simple 3D biped
  9. ImageNet classification with deep convolutional neural networks
  10. An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function

Their papers