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@inproceedings{1674916,
author = {Brázdil, Tomáš and Chatterjee, Krishnendu and Novotný, Petr and Vahala, Jiří},
address = {Palo Alto, California, USA},
booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020},
doi = {http://dx.doi.org/10.1609/aaai.v34i06.6531},
keywords = {reinforcement learning; Markov decision processes; Monte Carlo tree search; risk aversion},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Palo Alto, California, USA},
isbn = {978-1-57735-823-7},
pages = {9794-9801},
publisher = {AAAI Press},
title = {Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes},
url = {https://aaai.org/ojs/index.php/AAAI/article/view/6531},
year = {2020}
}
TY - JOUR
ID - 1674916
AU - Brázdil, Tomáš - Chatterjee, Krishnendu - Novotný, Petr - Vahala, Jiří
PY - 2020
TI - Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes
PB - AAAI Press
CY - Palo Alto, California, USA
SN - 9781577358237
KW - reinforcement learning
KW - Markov decision processes
KW - Monte Carlo tree search
KW - risk aversion
UR - https://aaai.org/ojs/index.php/AAAI/article/view/6531
L2 - https://aaai.org/ojs/index.php/AAAI/article/view/6531
N2 - Markov decision processes (MDPs) are the defacto framework for sequential decision making in the presence of stochastic uncertainty. A classical optimization criterion for MDPs is to maximize the expected discounted-sum payoff, which ignores low probability catastrophic events with highly negative impact on the system. On the other hand, risk-averse policies require the probability of undesirable events to be below a given threshold, but they do not account for optimization of the expected payoff. We consider MDPs with discounted-sum payoff with failure states which represent catastrophic outcomes. The objective of risk-constrained planning is to maximize the expected discounted-sum payoff among risk-averse policies that ensure the probability to encounter a failure state is below a desired threshold. Our main contribution is an efficient risk-constrained planning algorithm that combines UCT-like search with a predictor learned through interaction with the MDP (in the style of AlphaZero) and with a risk-constrained action selection via linear programming. We demonstrate the effectiveness of our approach with experiments on classical MDPs from the literature, including benchmarks with an order of 10^6 states.
ER -
BRÁZDIL, Tomáš, Krishnendu CHATTERJEE, Petr NOVOTNÝ a Jiří VAHALA. Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes. Online. In \textit{The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020}. Palo Alto, California, USA: AAAI Press, 2020, s.~9794-9801. ISBN~978-1-57735-823-7. Dostupné z: https://dx.doi.org/10.1609/aaai.v34i06.6531.
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