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Found 8 papers, 0 papers with code
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.
Data-driven control of COVID-19 in buildings: a reinforcement-learning approach
A general control framework is put forward for designing an optimal velocity field and proximal policy optimization, a reinforcement learning algorithm is employed to solve the control problem in a data-driven fashion.
A Two-Stage Mechanism for Demand Response Markets
Moreover, optimal power consumption reductions of the customers depend on the costs that they incur for curtailing consumption, which in general are private knowledge of the customers, and which they could strategically misreport in an effort to improve their own utilities even if it deteriorates the overall system cost.
Incentive Compatibility in Two-Stage Repeated Stochastic Games
In other settings such as iterative auctions or dynamic games where a large strategy space of this sort manifests, it typically has an important implication for mechanism design: It may be impossible to obtain truth-telling as a dominant strategy equilibrium.
Nonstochastic Bandits with Infinitely Many Experts
We prove a high-probability upper bound of $\tilde{\mathcal{O}} \big( i^*K + \sqrt{KT} \big)$ on the regret, up to polylog factors, where $i^*$ is the unknown position of the best expert, $K$ is the number of actions, and $T$ is the time horizon.
An Efficient and Incentive-Compatible Mechanism for Energy Storage Markets
A key obstacle to increasing renewable energy penetration in the power grid is the lack of utility-scale storage capacity.
Simple control for complex pandemics
The COVID-19 pandemic began over two years ago, yet schools, businesses, and other organizations are still struggling to keep the risk of disease outbreak low while returning to (near) normal functionality.
Data-driven control of micro-climate in buildings: an event-triggered reinforcement learning approach
Future of the smart buildings lies in using sensory data for adaptive decision making and control that is currently gloomed by the key challenge of learning a good control policy in a short period of time in an online and continuing fashion.
