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Found 7 papers, 1 papers with code
Joint Optimization for Coordinated Charging Control of Commercial Electric Vehicles Under Distributed Hydrogen Energy Supply
The transition to the zero-carbon power system is underway accelerating recently.
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method
Furthermore, convergence analysis is given under the discrete-space case, which guarantees that the policy will be reinforced by alternating between the processes of policy evaluation and policy improvement.
An Actor-Critic Method for Simulation-Based Optimization
We formulate the sampling process as a policy searching problem and give a solution from the perspective of Reinforcement Learning (RL).
A Two-phase On-line Joint Scheduling for Welfare Maximization of Charging Station
First, a joint scheduling model of pricing and charging control is developed to maximize the expected social welfare of the charging station considering the Quality of Service and the price fluctuation sensitivity of EV drivers.
Towards Efficient Dynamic Uplink Scheduling over Multiple Unknown Channels
To alleviate the challenge, we reformulate the problem as a variant of the restless multi-armed bandit (RMAB) problem and leverage Whittle's index theory to design an index-based scheduling policy algorithm.
Mind the Gap: Offline Policy Optimization for Imperfect Rewards
RGM is formulated as a bi-level optimization problem: the upper layer optimizes a reward correction term that performs visitation distribution matching w. r. t.
Query-Policy Misalignment in Preference-Based Reinforcement Learning
To unravel this mystery, we identify a long-neglected issue in the query selection schemes of existing PbRL studies: Query-Policy Misalignment.
