| DOI | Trouver le DOI : https://doi.org/10.1007/978-3-030-47358-7_6 |
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| Auteur | Rechercher : Bellinger, Colin1; Rechercher : Coles, Rory; Rechercher : Crowley, Mark; Rechercher : Tamblyn, Isaac2 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
- Conseil national de recherches du Canada. Technologies de sécurité et de rupture
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| Format | Texte, Article |
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| Conférence | Canadian Conference on Artificial Intelligence, Canadian AI 2020: Advances in Artificial Intelligence, May 13–15, 2020, Ottawa, Ontario, Canada |
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| Sujet | materials science; reinforcement learning; semi-Markov decision processes |
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| Résumé | Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design. Recent work includes, for example, the design of new structures and compositions of molecules for therapeutic drugs. Much of the existing work related to the application of RL to scientific domains, however, assumes that the available state representation obeys the Markov property. For reasons associated with time, cost, sensor accuracy, and gaps in scientific knowledge, many scientific design and discovery problems do not satisfy the Markov property. Thus, something other than a Markov decision process (MDP) should be used to plan/find the optimal policy. In this paper, we present a physics-inspired semi-Markov RL environment, namely the phase change environment. In addition, we evaluate the performance of value-based RL algorithms for both MDPs and partially observable MDPs (POMDPs) on the proposed environment. Our results demonstrate deep recurrent Q-networks (DRQN) significantly outperform deep Q-networks (DQN), and that DRQNs benefit from training with hindsight experience replay. Implications for the use of semi-Markovian RL and POMDPs for scientific laboratories are also discussed. |
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| Date de publication | 2020-05-06 |
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| Maison d’édition | Springer Nature Switzerland AG |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 3f553680-18d6-429c-8fc0-4c3ae973c0aa |
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| Enregistrement créé | 2020-07-24 |
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| Enregistrement modifié | 2022-01-14 |
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