Results 11 to 20 of about 544,487 (316)
, 2020 Chapter in "A Guided Tour of Artificial Intelligence Research ...
Buffet, Olivier +2 more
+7 more sources
Reinforcement Symbolic Learning [PDF]
, 2021 Complex problem solving involves representing structured knowledge, reasoning and learning, all at once. In this prospective study, we make explicit how a reinforcement learning paradigm can be applied to a symbolic representation of a concrete problem-solving task, modeled here by an ontology.
Mercier, Chloé +2 more
openaire +6 more sources
Memory-two strategies forming symmetric mutual reinforcement learning equilibrium in repeated prisoners' dilemma game [PDF]
Appl. Math. Comput. 444, 127819 (2023), 2021 We investigate symmetric equilibria of mutual reinforcement learning when both players alternately learn the optimal memory-two strategies against the opponent in the repeated prisoners' dilemma game. We provide a necessary condition for memory-two deterministic strategies to form symmetric equilibria.
arxiv +1 more source
Sensors, 2023 Reinforcement learning agents that have not been seen during training must be robust in test environments. However, the generalization problem is challenging to solve in reinforcement learning using high-dimensional images as the input. The addition of a
Sanghoon Park +4 more
doaj +1 more source
Advanced Engineering Materials, EarlyView., 2023 A novel method for tracking structural changes in gels using widely accessible microcomputed tomography is presented and validated for various hydro‐, alco‐, and aerogels. The core idea of the method is to track positions of micrometer‐sized tracer particles entrapped in the gel and relate them to the density of the gel network.
Anja Hajnal +3 more
wiley +1 more source
The Journal of Neuroscience, 2021 Humans routinely learn the value of actions by updating their expectations based on past outcomes – a process driven by reward prediction errors (RPEs). Importantly, however, implementing a course of action also requires the investment of effort. Recent work has revealed a close link between the neural signals involved in effort exertion and those ...
Huw Jarvis +5 more
openaire +4 more sources
A Survey on Reinforcement Learning Methods in Bionic Underwater Robots
Biomimetics, 2023 Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope.
Ru Tong +5 more
doaj +1 more source
A neural network model for the orbitofrontal cortex and task space acquisition during reinforcement learning. [PDF]
PLoS Computational Biology, 2018 Reinforcement learning has been widely used in explaining animal behavior. In reinforcement learning, the agent learns the value of the states in the task, collectively constituting the task state space, and uses the knowledge to choose actions and ...
Zhewei Zhang +4 more
doaj +1 more source
Algorithms, 2020 Reinforcement learning, as a branch of machine learning, has been gradually applied in the control field. However, in the practical application of the algorithm, the hyperparametric approach to network settings for deep reinforcement learning still ...
Menglin Li +3 more
doaj +1 more source
Review of Model-Based Reinforcement Learning
Jisuanji kexue yu tansuo, 2020 Deep reinforcement learning (DRL) as an important learning paradigm in the field of machine learning, has received increasing attentions after AlphaGo defeats the human.
ZHAO Tingting, KONG Le, HAN Yajie, REN Dehua, CHEN Yarui
doaj +1 more source