Jun Morimoto

Reinforcement Learning · 2000–2022

A researcher in the Science corpus: 11 papers; trusts 42 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 76 citations into it; #959 of 2,242 by trust here, #1,253 by citations
• Control and Systems Engineering — 35 citations into it; #835 of 8,257 by trust here, #747 by citations
• Artificial Intelligence — 34 citations into it; #6,436 of 35,345 by trust here, #11,624 by citations
• Biomedical Engineering — 27 citations into it; #877 of 10,734 by trust here, #2,870 by citations
• Cognitive Neuroscience — 16 citations into it; #4,828 of 10,852 by trust here, #4,022 by citations
• Robotics & Control — 12 citations into it
• Computational Theory and Mathematics — 10 citations into it; #1,399 of 12,182 by trust here, #2,060 by citations
• Social Psychology — 7 citations into it

Whom Jun Morimoto trusts overall

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

1. Kenji Doya
2. Richard S. Sutton
3. Stefan Schaal
4. Christopher G. Atkeson
5. Andrew Barto
6. Michael I. Jordan
7. Frank Kirchner
8. Jan Peters
9. Ronald J. Williams
10. Hajime Kimura

Who trusts Jun Morimoto overall

1. Jan Peters
2. Sergey Levine
3. Pieter Abbeel
4. J. Urgen Schmidhuber
5. Jens Kober
6. Gerhard Neumann
7. Stefan Schaal
8. Yuval Tassa
9. J. Bagnell
10. Chelsea Finn

Papers from Jun Morimoto's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Reinforcement Learning: An Introduction
7. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
8. Attention is All you Need (Neural Information Processing Systems)
9. GENERATIVE ADVERSARIAL NETS
10. ImageNet: A large-scale hierarchical image database

Their papers

• Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning (2000) — 269 citations
• Robust Reinforcement Learning (2005) — 460 citations
• Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation (2013) — 26 citations
• Learning from demonstration and adaptation of biped locomotion (2004) — 487 citations
• Deep learning, reinforcement learning, and world models (2022) — 503 citations
• Learning CPG-based Biped Locomotion with a Policy Gradient Method: Application to a Humanoid Robot (2008) — 296 citations
• Task-Specific Generalization of Discrete and Periodic Dynamic Movement Primitives (2010) — 379 citations
• Learning CPG-based biped locomotion with a policy gradient method (2005) — 32 citations
• Learning CPG Sensory Feedback with Policy Gradient for Biped Locomotion for a Full-Body Humanoid (2005) — 46 citations
• Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach (2002) — 90 citations
• Minimax Differential Dynamic Programming: An Application to Robust Biped Walking (2002) — 108 citations