Autonomous Agents and Multi-agent Systems Manuscript No. Learning Potential Functions and Their Representations for Multi-task Reinforcement Learning

By Matthijs Snel, Shimon Whiteson, S. Whiteson

42 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

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

In Artificial Intelligence

1. Sam Devlin
2. Ann Nowé
3. Peter Vrancx
4. Linda G. Bushnell
5. Radha Poovendran
6. Andrew Clark
7. Qifan Lu
8. Ani Harutyunyan

Citations in the corpus

• Expressing Arbitrary Reward Functions as Potential-Based Advice (2015, background): “This elegant and implementable framework led to an explosion of reward shaping research and proved to be extremely effective (Asmuth, Littman, and Zinkov 2008), (Devlin, Kudenko, and Grzes 2011), (Brys et al. 2014), (Snel and Whiteson 2014).”
• FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback (2020, methodology): “This has restricted the use of PBRS to tabular/ low-dimensional state spaces [15, 38].”