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Results for temporal difference learning

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14 papers · ranked by Valyu relevance

01
PPubMed12 May 2011
An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning Learning from Dopamine Signals
Wiebke Potjans, Markus Diesmann, Abigail Morrison, Tim Behrens
An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes…
90relevance
02
PPubMed11 Apr 2024
Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review
Mokhaled N. A. Al-Hamadani, Mohammed A. Fadhel, Laith Alzubaidi, Harangi Balazs + 2 more
'Harangi Balazs' 'Antonio Fernández-Caballero' 'Dominique Gruyer'] Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential…
90relevance
03
PPubMed23 Jul 2014
SSCC TD: A Serial and Simultaneous Configural-Cue Compound Stimuli Representation for Temporal Difference Learning Simultaneous and Serial Configurations for TD
Esther Mondragón, Jonathan Gray, Eduardo Alonso, Charlotte Bonardi + 2 more
This paper presents a novel representational framework for the Temporal Difference (TD) model of learning, which allows the computation of configural stimuli - cumulative compounds of stimuli that generate perceptual emergents known as configural cues. This Simultaneous and Serial Configural-cue Compound Stimuli…
89relevance
04
PPubMed20 Oct 2009
Temporal-Difference Reinforcement Learning with Distributed Representations Distributed RL with µAgents
Zeb Kurth-Nelson, A. David Redish, Olaf Sporns
Temporal-difference (TD) algorithms have been proposed as models of reinforcement learning (RL). We examine two issues of distributed representation in these TD algorithms: distributed representations of belief and distributed discounting factors. Distributed representation of belief allows the believed state of the…
88relevance
05
PPubMed6 Jul 2017
A neural model of hierarchical reinforcement learning A neural model of HRL
Daniel Rasmussen, Aaron Voelker, Chris Eliasmith, Gennady Cymbalyuk
We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and…
88relevance
06
PPubMed26 Aug 2022
Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review
Benton Girdler, William Caldbeck, Jihye Bae
Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of research that has been explored for decades in medicine, engineering, commercial, and machine-learning communities. In particular, the use of techniques using reinforcement learning (RL) has demonstrated impressive results but…
87relevance
07
PPubMed23 Jun 2021
Dynamical systems as a level of cognitive analysis of multi-agent learning Algorithmic foundations of temporal-difference learning dynamics
Wolfram Barfuss
A dynamical systems perspective on multi-agent learning, based on the link between evolutionary game theory and reinforcement learning, provides an improved, qualitative understanding of the emerging collective learning dynamics. However, confusion exists with respect to how this dynamical systems account of…
87relevance
08
PPubMed25 Aug 2025
Weber–Fechner law in temporal difference learning derived from control as inference Takahashi et al. 10.3389/frobt.2025.1649154
Keiichiro Takahashi, Taisuke Kobayashi, Tomoya Yamanokuchi, Takamitsu Matsubara
This study investigates a novel nonlinear update rule for value and policy functions based on temporal difference (TD) errors in reinforcement learning (RL). The update rule in standard RL states that the TD error is linearly proportional to the degree of updates, treating all rewards equally without any bias. On the…
86relevance
09
PPubMed11 Apr 2013
Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons Actor-Critic Learning with Spiking Neurons
Nicolas Frémaux, Henning Sprekeler, Wulfram Gerstner, Lyle J. Graham
Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning…
85relevance
10
PPubMed18 Aug 2023
Enhancing reinforcement learning models by including direct and indirect pathways improves performance on striatal dependent tasks Reinforcement learning models with direct and indirect pathways
Kim T. Blackwell, Kenji Doya, Ming Bo Cai
A major advance in understanding learning behavior stems from experiments showing that reward learning requires dopamine inputs to striatal neurons and arises from synaptic plasticity of cortico-striatal synapses. Numerous reinforcement learning models mimic this dopamine-dependent synaptic plasticity by using the…
85relevance
11
PPubMed19 Sep 2023
Learning to Express Reward Prediction Error-like Dopaminergic Activity Requires Plastic Representations of Time
Ian Cone, Claudia Clopath, Harel Z. Shouval
The dominant theoretical framework to account for reinforcement learning in the brain is temporal difference (TD) reinforcement learning. The TD framework predicts that some neuronal elements should represent the reward prediction error (RPE), which means they signal the difference between the expected future rewards…
84relevance
12
PPubMed17 Mar 2015
Kernel Temporal Differences for Neural Decoding
Jihye Bae, Luis G. Sanchez Giraldo, Eric A. Pohlmeyer, Joseph T. Francis + 2 more
'Joseph T. Francis' 'Justin C. Sanchez' 'José C. Príncipe'] We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This…
83relevance
13
PPubMed25 Sep 2022
Adaptive Discount Factor for Deep Reinforcement Learning in Continuing Tasks with Uncertainty
MyeongSeop Kim, Jung-Su Kim, Myoung-Su Choi, Jae-Han Park + 2 more
'Gianni D’Angelo' 'Arcangelo Castiglione'] Reinforcement learning (RL) trains an agent by maximizing the sum of a discounted reward. Since the discount factor has a critical effect on the learning performance of the RL agent, it is important to choose the discount factor properly. When uncertainties are involved in the…
78relevance
14
PPubMed27 Oct 2025
A neural network model combining the successor representation and actor-critic methods reveals effective biological use of the representation
Takayuki Tsurumi, Kenji Morita
In learning goal-directed behavior, state representation is important for adapting to the environment and achieving goals. A predictive state representation called successive representation (SR) has recently attracted attention as a candidate for state representation in animal brains, especially in the hippocampus. The…
63relevance

Results for temporal difference learning

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning Learning from Dopamine Signals

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

SSCC TD: A Serial and Simultaneous Configural-Cue Compound Stimuli Representation for Temporal Difference Learning Simultaneous and Serial Configurations for TD

Temporal-Difference Reinforcement Learning with Distributed Representations Distributed RL with µAgents

A neural model of hierarchical reinforcement learning A neural model of HRL

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

Dynamical systems as a level of cognitive analysis of multi-agent learning Algorithmic foundations of temporal-difference learning dynamics

Weber–Fechner law in temporal difference learning derived from control as inference Takahashi et al. 10.3389/frobt.2025.1649154

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons Actor-Critic Learning with Spiking Neurons

Enhancing reinforcement learning models by including direct and indirect pathways improves performance on striatal dependent tasks Reinforcement learning models with direct and indirect pathways

Learning to Express Reward Prediction Error-like Dopaminergic Activity Requires Plastic Representations of Time

Kernel Temporal Differences for Neural Decoding

Adaptive Discount Factor for Deep Reinforcement Learning in Continuing Tasks with Uncertainty

A neural network model combining the successor representation and actor-critic methods reveals effective biological use of the representation

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning Learning from Dopamine Signals

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

SSCC TD: A Serial and Simultaneous Configural-Cue Compound Stimuli Representation for Temporal Difference Learning Simultaneous and Serial Configurations for TD

Temporal-Difference Reinforcement Learning with Distributed Representations Distributed RL with µAgents

A neural model of hierarchical reinforcement learning A neural model of HRL

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

Dynamical systems as a level of cognitive analysis of multi-agent learning Algorithmic foundations of temporal-difference learning dynamics

Weber–Fechner law in temporal difference learning derived from control as inference Takahashi et al. 10.3389/frobt.2025.1649154

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons Actor-Critic Learning with Spiking Neurons

Enhancing reinforcement learning models by including direct and indirect pathways improves performance on striatal dependent tasks Reinforcement learning models with direct and indirect pathways

Learning to Express Reward Prediction Error-like Dopaminergic Activity Requires Plastic Representations of Time

Kernel Temporal Differences for Neural Decoding

Adaptive Discount Factor for Deep Reinforcement Learning in Continuing Tasks with Uncertainty

A neural network model combining the successor representation and actor-critic methods reveals effective biological use of the representation