Robust Multiobjective Reinforcement Learning Considering Environmental Uncertainties
Numerous real-world decision or control problems involve multiple conflicting objectives
whose relative importance (preference) is required to be weighed in different scenarios …
whose relative importance (preference) is required to be weighed in different scenarios …
Deep reinforcement learning-based optimal bidding strategy for real-time multi-participant electricity market with short-term load
This paper aims to address the bidding strategy optimization in the real-time multi-participant
electricity market with short-term load dynamics. In order to avoid the sub-optimal solution …
electricity market with short-term load dynamics. In order to avoid the sub-optimal solution …
Safe reinforcement learning using finite-horizon gradient-based estimation
A key aspect of Safe Reinforcement Learning (Safe RL) involves estimating the constraint
condition for the next policy, which is crucial for guiding the optimization of safe policy …
condition for the next policy, which is crucial for guiding the optimization of safe policy …
Multi-objective reinforcement learning based on nonlinear scalarization and long-short-term optimization
H Wang - Robotic Intelligence and Automation, 2024 - emerald.com
Purpose Many practical control problems require achieving multiple objectives, and these
objectives often conflict with each other. The existing multi-objective evolutionary …
objectives often conflict with each other. The existing multi-objective evolutionary …
Интеллектуальные робастные контроллеры триботронных конических опор скольжения
ЮН Казаков, ДВ Шутин, ЛА Савин - VESTNIK of Samara …, 2024 - journals.ssau.ru
Триботронные опорные узлы представляют собой мультифизическую систему,
основанную на совокупности гидродинамических, теплофизических, динамических …
основанную на совокупности гидродинамических, теплофизических, динамических …