- Tytuł:
- Model-building adaptive critics for semi-Markov control
- Autorzy:
-
Gosavi, A.
Murray, S.
Hu, J.
Ghosh, S. - Powiązania:
- https://bibliotekanauki.pl/articles/91878.pdf
- Data publikacji:
- 2012
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
adaptive critics
learning algorithm
semi-Markov process
decision process - Opis:
- Adaptive (or actor) critics are a class of reinforcement learning algorithms. Generally, in adaptive critics, one starts with randomized policies and gradually updates the probability of selecting actions until a deterministic policy is obtained. Classically, these algorithms have been studied for Markov decision processes under model-free updates. Algorithms that build the model are often more stable and require less training in comparison to their model-free counterparts. We propose a new model-building adaptive critic, which builds the model during the learning, for a discounted-reward semi-Markov decision process under some assumptions on the structure of the process. We illustrate the use of our algorithm with numerical results on a system with 10 states and a real-world case-study from management science.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 1; 43-58
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł