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Found 6 papers, 1 papers with code
Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system
However, the adoption of CDRT is not economically viable at the current carbon price.
Incentive-aware Electric Vehicle Routing Problem: a Bi-level Model and a Joint Solution Algorithm
Specifically, we first devise a bi-level model whereby the fleet operator optimizes the routes and charging schedules of the fleet jointly with an incentive rate to reimburse the delivery delays experienced by the customers.
Cooperative Operation of the Fleet Operator and Incentive-aware Customers in an On-demand Delivery System: A Bi-level Approach
In this paper, we study the cooperative operation problem between the fleet operator and incentive-aware customers in an on-demand delivery system.
Optimal Estimator Design and Properties Analysis for Interconnected Systems with Asymmetric Information Structure
We found that there exists a critical probability such that the EEC is bounded if the delay probability is below the critical probability.
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation
Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice.
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
The present paper develops a novel aggregated gradient approach for distributed machine learning that adaptively compresses the gradient communication.
