4 citations · 9 across the 6 of their papers we have counts for
6 papers
A Bayesian Approach to Robust Inverse Reinforcement Learning
Ran Wei, Siliang Zeng, Chenliang Li +3
We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL). The proposed framework differs from existing offline model-based IRL approaches by perf…
Learning An Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following
Ran Wei, Anthony D. McDonald, Alfredo Garcia +3
In this paper we introduce a general estimation methodology for learning a model of human perception and control in a sensorimotor control task based upon a finite set of demonstra…
When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
Siliang Zeng, Chenliang Li, Alfredo Garcia +1
Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demo…
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
Siliang Zeng, Chenliang Li, Alfredo Garcia +1
Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy that best fits observed sequences of states and actions implemented by an…
Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees
Siliang Zeng, Mingyi Hong, Alfredo Garcia
We consider the task of estimating a structural model of dynamic decisions by a human agent based upon the observable history of implemented actions and visited states. This proble…
Structural Estimation of Partially Observable Markov Decision Processes
Yanling Chang, Alfredo Garcia, Zhide Wang +1
In many practical settings control decisions must be made under partial/imperfect information about the evolution of a relevant state variable. Partially Observable Markov Decision…