David L. Roberts

Reinforcement Learning · 2011–2018

A researcher in the Science corpus: 5 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 30 citations into it
• Artificial Intelligence — 22 citations into it
• Control and Systems Engineering — 12 citations into it

Whom David L. Roberts trusts overall

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

1. W. B. Knox
2. Richard S. Sutton

Papers from David L. Roberts's perspective

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

1. Reinforcement Learning: An Introduction
2. Algorithms for Inverse Reinforcement Learning
3. Maximum Entropy Inverse Reinforcement Learning
4. Residual Algorithms: Reinforcement Learning with Function Approximation
5. A survey of robot learning from demonstration
6. Policy Gradient Methods for Reinforcement Learning with Function Approximation
7. Teachable robots: Understanding human teaching behavior to build more effective robot learners
8. The Behavior of Organisms: An Experimental Analysis
9. Dynamic Reward Shaping: Training a Robot by Voice
10. Robot learning from demonstration

Their papers

• Interactive Learning from Policy-Dependent Human Feedback (2017) — 346 citations
• Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning (2015) — 109 citations
• A review of the trade in orchids and its implications for conservation (2018) — 315 citations
• Learning something from nothing: Leveraging implicit human feedback strategies (2014) — 31 citations
• How many herbarium specimens are needed to detect threatened species (2011) — 148 citations