The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper focuses on implementing a reinforcement learning algorithm for solving parameter optimization problems of Proportional Integral Derivative (PID) controllers. Function Optimization by Reinforcement Learning (FORL) remarkably outperforms a number of population-based intelligent algorithms when executed on benchmark functions in high-dimension circumstances. Therefore, this paper aims at examining...
This paper presents a new method called Multi-objective Optimization by Reinforcement Learning (MORL), to solve the optimal power system dispatch and voltage stability problem. In MORL, the search is undertaken on individual dimension in a high-dimensional space via a path selected by an estimated path value which represents the potential of finding a better solution. MORL is compared with multi-objective...
This paper presents a new algorithm, Function Optimisation by Reinforcement Learning (FORL), to solve large-scale and complex function optimisation problems, in particular for those in a high-dimensional space. FORL undertakes the dimensional search in sequence, in contrast to evolutionary algorithms (EAs) which are based on the population-based search, and has the ability of memory of history incorporated...
This paper presents a multi-objective optimisation by reinforcement learning, called MORL, to solve complex multi-objective optimisation problems, in particular those in a high-dimensional space. In MORL, the search is undertaken on individual dimension in a high-dimensional space via a path selected by an estimated path value. Path values, estimated by weighting the state values on the selected path,...
This paper presents a new algorithm, Function Optimisation by Reinforcement Learning (FORL), to solve large-scale and complex function optimisation problems. FORL undertakes the dimensional search in sequence, in contrast to evolutionary algorithms (EAs) which are based on the population-based search, and has the ability of memory of history incorporated via estimating and updating of the values of...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.