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Found 4 papers, 0 papers with code
Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map (extended version)
We provide two main theoretical results: 1) characterize the number of taxis $m$ that is sufficient for IA to be stable; 2) derive a necessary condition on $m$ to maintain stability for IA as time goes to infinity.
Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand
We propose a mechanism for switching the originally trained offline approximation when the current demand is outside the original validity region.
Multiagent Rollout and Policy Iteration for POMDP with Application to Multi-Robot Repair Problems
Our methods specifically address the computational challenges of partially observable multiagent problems.
Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration with Application to Autonomous Sequential Repair Problems
In this paper we consider infinite horizon discounted dynamic programming problems with finite state and control spaces, and partial state observations.
