Potential-based reward shaping for finite horizon online POMDP planning

By Adam Eck, Leen-Kiat Soh, Sam Devlin, Daniel Kudenko. 2016

39 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Reinforcement Learning

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. Sergey Levine
2. Wojciech M. Czarnecki
3. Karl Tuyls
4. Thore Graepel
5. Joel Z. Leibo
6. Marc Lanctot
7. Max Jaderberg
8. Shimon Whiteson

In Reinforcement Learning

1. Karl Tuyls
2. Shimon Whiteson
3. Joel Z. Leibo
4. Thore Graepel
5. Marc Lanctot
6. Wojciech M. Czarnecki
7. Sergey Levine
8. Vinícius Flores Zambaldi

Citations in the corpus

• Value-Decomposition Networks For Cooperative Multi-Agent Learning (2017, background): “However, even in the single-agent case, reward shaping is difficult and only a small class of shaped reward functions are guaranteed to preserve optimality w.r.t. the true objective (Ng et al., 1999; Devlin et al., 2014; Eck et al., 2016).”
• A review of cooperative multi-agent deep reinforcement learning (2019, background): “This approach is also extended for online POMDP settings [33] and multi-agent setting [30], though defining the potential function is challenging and usually needs specific domain knowledge.”