Name
Title | ||
|---|---|---|
Learning to Act Optimally in Partially Observable Multiagent Settings: (Doctoral Consortium). |
Abstract | ||
|---|---|---|
My research is focused on modeling optimal decision making in partially observable multiagent environments. I began with an investigation into the cognitive biases that induce subnormative behavior in humans playing games online in multiagent settings, leveraging well-known computational psychology approaches in modeling humans playing a strategic, sequential game. My subsequent work was in a scalable extension to Monte Carlo exploring starts for POMDPs (MCES-P), where I expanded the theory and algorithm to the multiagent setting. I first introduced a straightforward application with probably approximately correct guarantees (MCESP+PAC), and then introduced a more sample efficient partially model-based framework (MCESIP+PAC) that explicitly modeled the opponent. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5555/2936924.2937241 | AAMAS |
Field | DocType | ISBN |
Optimal decision,Observable,Probably approximately correct learning,Computer science,Artificial intelligence,Sequential game,Machine learning,Scalability,Reinforcement learning | Conference | 978-1-4503-4239-1 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
1 | ||