1 citations · 1 across the 3 of their papers we have counts for
3 papers
Optimally Solving Simultaneous-Move Dec-POMDPs: The Sequential Central Planning Approach
Johan Peralez, Aurèlien Delage, Jacopo Castellini +2
The centralized training for decentralized execution paradigm emerged as the state-of-the-art approach to -optimally solving decentralized partially observable Markov decision p…
Solving Hierarchical Information-Sharing Dec-POMDPs: An Extensive-Form Game Approach
Johan Peralez, Aurélien Delage, Olivier Buffet +1
A recent theory shows that a multi-player decentralized partially observable Markov decision process can be transformed into an equivalent single-player game, enabling the applicat…
On Convex Optimal Value Functions For POSGs
Rafael F. Cunha, Jacopo Castellini, Johan Peralez +1
Multi-agent planning and reinforcement learning can be challenging when agents cannot see the state of the world or communicate with each other due to communication costs, latency,…