Matthew J. Howard

Reinforcement Learning · 2010–2017

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 5 citations into it

Whom Matthew J. Howard trusts overall

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

1. Radford M. Neal
2. Neil D. Lawrence
3. Andrew Barto
4. Florent Guenter
5. Richard S. Sutton

Who trusts Matthew J. Howard overall

1. Jens Kober
2. Katharina Muelling

Papers from Matthew J. Howard's perspective

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

1. Reinforcement Learning: An Introduction
2. Pattern Recognition and Machine Learning
3. Robot Programming by Demonstration
4. Recent Advances in Hierarchical Reinforcement Learning
5. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
6. Towards a Unified Theory of State Abstraction for MDPs
7. Full Paper Reinforcement Learning for Imitating Constrained Reaching Movements
8. Style-based inverse kinematics (ACM Transactions on Graphics)
9. A unified approach for motion and force control of robot manipulators: The operational space formulation
10. Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining

Their papers

• Using dimensionality reduction to exploit constraints in reinforcement learning (2010) — 55 citations
• Learning task constraints in operational space formulation (2017) — 45 citations