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Found 7 papers, 3 papers with code
Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming
The integration of physiological computing into mixed-initiative human-robot interaction systems offers valuable advantages in autonomous task allocation by incorporating real-time features as human state observations into the decision-making system.
Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning
Offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task that greatly depends on the data available in the learning phase.
Expert-Guided Symmetry Detection in Markov Decision Processes
Learning a Markov Decision Process (MDP) from a fixed batch of trajectories is a non-trivial task whose outcome's quality depends on both the amount and the diversity of the sampled regions of the state-action space.
An Offline Risk-aware Policy Selection Method for Bayesian Markov Decision Processes
In Offline Model Learning for Planning and in Offline Reinforcement Learning, the limited data set hinders the estimate of the Value function of the relative Markov Decision Process (MDP).
Offline Learning for Planning: A Summary
Offline learning is the area of machine learning concerned with efficiently obtaining an optimal policy with a batch of previously collected experiences without further interaction with the environment.
One Class Splitting Criteria for Random Forests
Random Forests (RFs) are strong machine learning tools for classification and regression.
Qualitative Possibilistic Mixed-Observable MDPs
Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POMDPs used to model situations where the agent's initial belief or observation probabilities are imprecise due to lack of past experiences or insufficient data collection.
