AIMC Topic: Reinforcement, Psychology

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Model-based reinforcement learning with dimension reduction.

Neural networks : the official journal of the International Neural Network Society Aug 24, 2016
The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derive...
Artificial Intelligence Machine Learning Reward Entropy Reinforcement, Psychology Learning Models, Theoretical Least-Squares Analysis
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Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity.

Frontiers in neural circuits Jul 21, 2016
The brain enables animals to behaviorally adapt in order to survive in a complex and dynamic environment, but how reward-oriented behaviors are achieved and computed by its underlying neural circuitry is an open question. To address this concern, we ...
Neuronal Plasticity Parkinson Disease Reward Humans Neural Networks, Computer Neural Pathways Reinforcement, Psychology Basal Ganglia
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Extending unified-theory-of-reinforcement neural networks to steady-state operant behavior.

Behavioural processes Mar 24, 2016
The unified theory of reinforcement has been used to develop models of behavior over the last 20 years (Donahoe et al., 1993). Previous research has focused on the theory's concordance with the respondent behavior of humans and animals. In this exper...
Reinforcement Schedule Choice Behavior Psychological Theory Conditioning, Operant Neural Networks, Computer Reinforcement, Psychology
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Machine Learning Capabilities of a Simulated Cerebellum.

IEEE transactions on neural networks and learning systems Jan 26, 2016
This paper describes the learning and control capabilities of a biologically constrained bottom-up model of the mammalian cerebellum. Results are presented from six tasks: 1) eyelid conditioning; 2) pendulum balancing; 3) proportional-integral-deriva...
Humans Neural Networks, Computer Machine Learning Cerebellum Models, Neurological Reinforcement, Psychology Postural Balance Pattern Recognition, Physiological Neurons Conditioning, Eyelid Computer Simulation Animals
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Behavioral plasticity through the modulation of switch neurons.

Neural networks : the official journal of the International Neural Network Society Nov 12, 2015
A central question in artificial intelligence is how to design agents capable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural ...
Neurosciences Neurons Synapses Maze Learning Problem Solving Behavior Reinforcement, Psychology Computer Simulation Neuronal Plasticity Signal Processing, Computer-Assisted Machine Learning Algorithms Neural Networks, Computer Artificial Intelligence
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Goal-oriented robot navigation learning using a multi-scale space representation.

Neural networks : the official journal of the International Neural Network Society Oct 19, 2015
There has been extensive research in recent years on the multi-scale nature of hippocampal place cells and entorhinal grid cells encoding which led to many speculations on their role in spatial cognition. In this paper we focus on the multi-scale nat...
Hippocampus Goals Reinforcement, Psychology Spatial Navigation Cognition Models, Neurological Robotics Humans Learning
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Unified-theory-of-reinforcement neural networks do not simulate the blocking effect.

Behavioural processes Aug 28, 2015
For the last 20 years the unified theory of reinforcement (Donahoe et al., 1993) has been used to develop computer simulations to evaluate its plausibility as an account for behavior. The unified theory of reinforcement states that operant and respon...
Neural Networks, Computer Computer Simulation Data Interpretation, Statistical Reinforcement, Psychology Conditioning, Classical Conditioning, Operant Reinforcement Schedule
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Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

Neural networks : the official journal of the International Neural Network Society Aug 24, 2015
The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE a...
Reinforcement, Psychology Nonlinear Dynamics Problem Solving Algorithms Neural Networks, Computer Computer Simulation Machine Learning Models, Theoretical
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A new computational account of cognitive control over reinforcement-based decision-making: Modeling of a probabilistic learning task.

Neural networks : the official journal of the International Neural Network Society Aug 20, 2015
Recent work on decision-making field offers an account of dual-system theory for decision-making process. This theory holds that this process is conducted by two main controllers: a goal-directed system and a habitual system. In the reinforcement lea...
Humans Machine Learning Decision Making Reinforcement, Psychology Adaptation, Psychological Algorithms Mental Processes Models, Statistical Evoked Potentials Reaction Time Obsessive-Compulsive Disorder Computer Simulation Cognition
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Kernel temporal differences for neural decoding.

Computational intelligence and neuroscience Mar 17, 2015
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. T...
Reinforcement, Psychology Computer Simulation Artificial Intelligence Models, Neurological Algorithms Brain-Computer Interfaces
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