Action control, forward models and expected rewards: representations in reinforcement learning
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1573-0964
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Action control, forward models and expected rewards: representations in reinforcement learning ; volume:199 ; number:5-6 ; day:1 ; month:11 ; year:2021 ; pages:14017-14033 ; date:12.2021
Synthese; 199, Heft 5-6 (1.11.2021), 14017-14033, 12.2021
- Klassifikation
-
Philosophie
- Urheber
-
Rusanen, Anna-Mari
Lappi, Otto
Kuokkanen, Jesse
Pekkanen, Jami
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s11229-021-03408-w
- URN
-
urn:nbn:de:101:1-2022042401234427772880
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:29 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Rusanen, Anna-Mari
- Lappi, Otto
- Kuokkanen, Jesse
- Pekkanen, Jami
- SpringerLink (Online service)
Ähnliche Objekte (12)
Under-Approximating Expected Total Rewards in POMDPs
Reinforcement Learning with Sparse and Multiple Rewards
Expected multi-utility representations
Adaptive representations for reinforcement learning
Uncertainty Representations in Reinforcement Learning
Action sharpens sensory representations of expected outcomes
Forward-backward systems for expected utility maximization
Forward-backward systems for expected utility maximization
Dense Rewards and Continual Reinforcement Learning for Task-oriented Dialogue Policies
Simultaneous estimation of rewards and dynamics in inverse reinforcement learning problems
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards
Hierarchical reinforcement learning for handling sparse rewards in multi-goal navigation
Under-Approximating Expected Total Rewards in POMDPs
Reinforcement Learning with Sparse and Multiple Rewards
Expected multi-utility representations
Adaptive representations for reinforcement learning
Uncertainty Representations in Reinforcement Learning
Action sharpens sensory representations of expected outcomes
Forward-backward systems for expected utility maximization
Forward-backward systems for expected utility maximization
Dense Rewards and Continual Reinforcement Learning for Task-oriented Dialogue Policies
Simultaneous estimation of rewards and dynamics in inverse reinforcement learning problems
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards
Hierarchical reinforcement learning for handling sparse rewards in multi-goal navigation
Under-Approximating Expected Total Rewards in POMDPs
Reinforcement Learning with Sparse and Multiple Rewards
Expected multi-utility representations
Adaptive representations for reinforcement learning
Uncertainty Representations in Reinforcement Learning
Action sharpens sensory representations of expected outcomes
Forward-backward systems for expected utility maximization
Forward-backward systems for expected utility maximization
Dense Rewards and Continual Reinforcement Learning for Task-oriented Dialogue Policies
Simultaneous estimation of rewards and dynamics in inverse reinforcement learning problems
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards