Policy Search using Paired Comparisons

By Malcolm J. A. Strens, Andrew W. Moore. 2003

37 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sergey Levine
2. Yoshua Bengio
3. Pieter Abbeel
4. Ziyun Wang
5. Chelsea Finn
6. Trevor Darrell
7. Ruslan Salakhutdinov
8. Abhinav Kumar Gupta

Citations in the corpus

• A Framework for Data-Driven Robotics (2019, methodology): “Preference learning is also used in RL for reward learning [52], [53].”
• Scaling data-driven robotics with reward sketching and batch reinforcement learning (2019, methodology): “Preference elicitation is also used for reward learning in RL [65, 71].”
• A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm (2004, background): “There have been some studies to increase the convergence speed of the DE algorithm [8, 9].”
• Bayesian sparse sampling for on-line reward optimization (2005, background): “The policy based approach tries to estimate a good policy directly (Ng & Jordan, 2000; Strens & Moore, 2002).”
• Hierarchical Relative Entropy Policy Search (2014, background): “Here, a gating network first decides which of the options to execute and, subsequently, the option-policy determines the action.”