Sampling based approaches for minimizing regret in uncertain Markov Decision Problems (MDPs)

• Main Authors: AHMED, Asrar, VARAKANTHAM, Pradeep, LOWALEKAR, Meghna, ADULYASAK, Yossiri, JAILLET, Patrick
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Artificial Intelligence and Robotics; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/3937; https://ink.library.smu.edu.sg/context/sis_research/article/4939/viewcontent/Sampling_based_approach_regret_MDP_JAIR_pv.pdf
• Institution: Singapore Management University

Markov Decision Processes (MDPs) are an effective model to represent decision processes in the presence of transitional uncertainty and reward tradeoffs. However, due to the difficulty in exactly specifying the transition and reward functions in MDPs, researchers have proposed uncertain MDP models a...