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.
In this paper, we present a recursive least squares approximate policy iteration (RLSAPI) algorithm for infinite-horizon multi-dimensional Markov decision process in continuous state and action spaces. Under certain problem structure assumptions on value functions and policy spaces, the approximate policy iteration algorithm is provably convergent in the mean. That is to say the mean absolute deviation...
We consider finite-state Markov decision processes, and prove convergence and rate of convergence results for certain least squares policy evaluation algorithms of the type known as LSPE(lambda ). These are temporal difference methods for constructing a linear function approximation of the cost function of a stationary policy, within the context of infinite-horizon discounted and average cost dynamic...
A novel infinite-horizon policy-gradient estimation method with variable discount factor is proposed in this paper. This method tackles the normal policy-gradient estimation methods' limitations on unbalance of the bias and variance by using an incremental sequence as the discount factor. Numerical experiments conducted on the Markov decision process have shown its effectiveness.
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.