Technical Note: \cal Q-Learning
Machine Learning
Actor-critic models of the basal ganglia: new anatomical and computational perspectives
Neural Networks - Computational models of neuromodulation
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Distributed real time neural networks in interactive complex systems
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
In this paper we present a model of reinforcement learning (RL) which can be used to solve goal-oriented navigation tasks. Our model supposes that transitions between places are learned in the hippocampus (CA pyramidal cells) and associated with information coming from path-integration. The RL neural network acts as a bias on these transitions to perform action selection. RL originates in the basal ganglia and matches observations of reward-based activity in dopaminergic neurons. Experiments were conducted in a simulated environment. We show that our model using transitions and inspired by Q-learning performs more efficiently than traditional actor-critic models of the basal ganglia based on temporal difference (TD) learning and using static states.