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Found 8 papers, 0 papers with code
Delayed Geometric Discounts: An Alternative Criterion for Reinforcement Learning
The endeavor of artificial intelligence (AI) is to design autonomous agents capable of achieving complex tasks.
Unsupervised Neural Hidden Markov Models with a Continuous latent state space
We introduce a new procedure to neuralize unsupervised Hidden Markov Models in the continuous case.
Offline Inverse Reinforcement Learning
Current solutions either solve a behaviour cloning problem (which does not leverage the exploratory data) or a reinforced imitation learning problem (using a fixed cost function that discriminates available exploratory trajectories from expert ones).
Quickest change detection with unknown parameters: Constant complexity and near optimality
We consider the quickest change detection problem where both the parameters of pre- and post- change distributions are unknown, which prevents the use of classical simple hypothesis testing.
A Generalised Inverse Reinforcement Learning Framework
The gloabal objective of inverse Reinforcement Learning (IRL) is to estimate the unknown cost function of some MDP base on observed trajectories generated by (approximate) optimal policies.
Quickest change detection for multi-task problems under unknown parameters
We consider the quickest change detection problem where both the parameters of pre- and post- change distributions are unknown, which prevent the use of classical simple hypothesis testing.
Trajectory representation learning for Multi-Task NMRDPs planning
Expanding Non Markovian Reward Decision Processes (NMRDP) into Markov Decision Processes (MDP) enables the use of state of the art Reinforcement Learning (RL) techniques to identify optimal policies.
Markov Decision Process for MOOC users behavioral inference
Studies on massive open online courses (MOOCs) users discuss the existence of typical profiles and their impact on the learning process of the students.
