On Modeling Human Learning in Sequential Games with Delayed Reinforcements. - Roi Ceren; Prashant Doshi; Matthew Meisel; Adam Goodie; Dan Hall (SMC, 2013)

Learning to Act Optimally in Partially Observable Multiagent Settings: (Doctoral Consortium). - Roi Ceren (AAMAS, 2016)

Reinforcement Learning in Partially Observable Multiagent Settings: Monte Carlo Exploring Policies with PAC Bounds. - Roi Ceren; Prashant Doshi; Bikramjit Banerjee (AAMAS, 2016)

Reinforcement Learning for Heterogeneous Teams with PALO Bounds. - Roi Ceren; Prashant Doshi; Keyang He (CoRR, 2018)

Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning. - Roi Ceren (CoRR, 2019)

Towards a Decentralized, Autonomous Multiagent Framework for Mitigating Crop Loss. - Roi Ceren; Shannon Quinn; Glen Raines (CoRR, 2019)

PALO bounds for reinforcement learning in partially observable stochastic games. - Roi Ceren; Keyang He; Prashant Doshi; Bikramjit Banerjee (Neurocomputing, 2021)