proroklab/rllib_differentiable_comms
This is a minimal example to demonstrate how multi-agent reinforcement learning with differentiable communication channels and centralized critics can be realized in RLLib. This example serves as a reference implementation and starting point for making RLLib more compatible with such architectures.
Python
Stargazers
- AcciorocketshipsUniversity of Cambridge
- ben-hudsonBlaise Transit
- dbsxodud-11KAIST SILAB
- DJayalathUniversity of Oxford
- DylanCopeLondon
- Filocava99Blue Reply S.r.l.
- harshakokelIBM Research
- jakobhartmann
- janblumenkampUniversity of Cambridge
- JiekaiJiaEcovacs Robotics
- kiranikramLondon
- krzjoaCEVA Logistics
- liuyaoquan
- ltzhengNanyang Technological University
- MarwanMousaImperial College London
- matteobettiniUniversity of Cambridge
- maxstanden
- NemaVatsalaIISER Bhopal
- nmatareNew York City, New York
- pengzhenghaoLos Angeles
- Peter010103https://proroklab.org/wp/team/
- pthpth
- ranzuhEspoo, Finland
- Rex18lf
- RiccZamboniPoliMi
- RocketRiderAirbus Defence and Space
- Rohanjames1997
- seiing@holiday-robot
- singh-jayant
- SOUMAJYOTIAWS
- TheohhhuAustralia
- TimeEscaperRussia
- weyenrooney
- wullli@hslu-abiz
- yubryanj
- zihaodeng00Amherst, MA