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pro vyhledávání: '"Szita, István"'
Autor:
Kurti, Zsuzsanna, David, Gyula, Erdelyi, Zsuzsanna, Szita, Istvan, Pandur, Tunde, Al Khoury, Alex, Wetwittayakhlang, Panu, Golovics, Petra A., Gonczi, Lorant, Lakatos, Laszlo, Lakatos, Peter L.
Publikováno v:
In Clinical Gastroenterology and Hepatology January 2024 22(1):191-193
This paper presents a new algorithm for online linear regression whose efficiency guarantees satisfy the requirements of the KWIK (Knows What It Knows) framework. The algorithm improves on the complexity bounds of the current state-of-the-art procedu
Externí odkaz:
http://arxiv.org/abs/1205.2606
Autor:
Szita, Istvan, Lorincz, Andras
In this paper we propose an algorithm for polynomial-time reinforcement learning in factored Markov decision processes (FMDPs). The factored optimistic initial model (FOIM) algorithm, maintains an empirical model of the FMDP in a conventional way, an
Externí odkaz:
http://arxiv.org/abs/0904.3352
Akademický článek
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Autor:
Szita, István, Lőrincz, András
The exploration-exploitation dilemma has been an intriguing and unsolved problem within the framework of reinforcement learning. "Optimism in the face of uncertainty" and model building play central roles in advanced exploration methods. Here, we int
Externí odkaz:
http://arxiv.org/abs/0810.3451
Autor:
Szita, Istvan, Lorincz, Andras
In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solution of factored Markov decision processes (fMDPs). The traditional approximate value iteration algorithm is modified in two ways. For one, the least-
Externí odkaz:
http://arxiv.org/abs/0801.2069
Autor:
Szita, Istvan, Lorincz, Andras
The cross-entropy method is a simple but efficient method for global optimization. In this paper we provide two online variants of the basic CEM, together with a proof of convergence.
Comment: 8 pages
Comment: 8 pages
Externí odkaz:
http://arxiv.org/abs/0801.1988
Autor:
Szita, Istvan, Lorincz, Andras
In this paper we propose a method that learns to play Pac-Man. We define a set of high-level observation and action modules. Actions are temporally extended, and multiple action modules may be in effect concurrently. A decision of the agent is repres
Externí odkaz:
http://arxiv.org/abs/cs/0610170
Autor:
Szita, Istvan, Lorincz, Andras
Reinforcement learning is commonly used with function approximation. However, very few positive results are known about the convergence of function approximation based RL control algorithms. In this paper we show that TD(0) and Sarsa(0) with linear f
Externí odkaz:
http://arxiv.org/abs/cs/0306120
Autor:
Szita, Istvan, Lorincz, Andras
There is a growing interest in using Kalman-filter models in brain modelling. In turn, it is of considerable importance to make Kalman-filters amenable for reinforcement learning. In the usual formulation of optimal control it is computed off-line by
Externí odkaz:
http://arxiv.org/abs/cs/0301007