Search Results for author:
Found 7 papers, 1 papers with code
The Nah Bandit: Modeling User Non-compliance in Recommendation Systems
It is thus crucial in cyber-physical recommendation systems to operate with an interaction model that is aware of such user behavior, lest the user abandon the recommendations altogether.
Model-Based Transfer Learning for Contextual Reinforcement Learning
Deep reinforcement learning (RL) is a powerful approach to complex decision making.
Cooperative Advisory Residual Policies for Congestion Mitigation
Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers.
Expert with Clustering: Hierarchical Online Preference Learning Framework
To the best of the authors knowledge, this is the first work to analyze the regret of an integrated expert algorithm with k-Means clustering.
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
We introduce Temporal Transfer Learning (TTL) algorithms to select source tasks for zero-shot transfer, systematically leveraging the temporal structure to solve the full range of tasks.
Incentive Design for Eco-driving in Urban Transportation Networks
Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks.
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP.
