Solving uncertain MDPs with objectives that are separable over instantiations of model uncertainty
Markov Decision Problems, MDPs offer an effective mechanism for planning under uncertainty. However, due to unavoidable uncertainty over models, it is difficult to obtain an exact specification of an MDP. We are interested in solving MDPs, where transition and reward functions are not exactly specif...
Main Authors: | Adulyasak, Yossiri, Varakantham, Pradeep, Ahmed, Asrar, Jaillet, Patrick |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
Format: | Article |
Language: | en_US |
Published: |
AAAI Press
2018
|
Online Access: | http://hdl.handle.net/1721.1/116234 https://orcid.org/0000-0002-8585-6566 |
Similar Items
-
Sampling Based Approaches for Minimizing Regret in Uncertain Markov Decision Processes (MDPs)
by: Ahmed, Asrar, et al.
Published: (2021) -
Regret Based Robust Solutions for Uncertain Markov Decision Processes
by: Ahmed, Asrar, et al.
Published: (2015) -
Decentralized stochastic planning with anonymity in interactions
by: Varakantham, Pradeep, et al.
Published: (2015) -
Dynamic Redeployment to Counter Congestion or Starvation in Vehicle Sharing Systems
by: Ghosh, Supriyo, et al.
Published: (2015) -
Dynamic Repositioning to Reduce Lost Demand in Bike Sharing Systems
by: Ghosh, Supriyo, et al.
Published: (2017)