Simulation of adaptive behavior in animats: review and prospect
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Learning to Perceive and Act by Trial and Error
Machine Learning
Emergence of functional modularity in robots
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Adaptive mixtures of local experts
Neural Computation
Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Levels and Types of Action Selection: The Action Selection Soup
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Evolution and learning in an intrinsically motivated reinforcement learning robot
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
A computational model of habit learning to enable ambient support for lifestyle change
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
A model of reaching that integrates reinforcement learning and population encoding of postures
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
iFALCON: A neural architecture for hierarchical planning
Neurocomputing
Hedonic value: enhancing adaptation for motivated agents
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Hi-index | 0.00 |
This work presents a modular neural-network model (based on reinforcement-learning actor-critic methods) that tries to capture some of the most relevant known aspects of the role that basal ganglia play in learning and selecting motor behavior related to different goals. The model uses a mixture of experts network for the critic and a hierarchical network with two levels for the actor. Some simulations with the model show that basal ganglia select 'chunks' of behavior whose 'details' are specified by direct sensory-motor pathways, and how emergent modularity can help to deal with tasks with asynchronous multiple goals. A 'top-down' approach is adopted that first analyses some adaptive non-trivial interaction of a whole (simulated) organism with the environment, and its capacity to learn, and then attempts to implement these functions with neural architectures and mechanisms that have an empirical neuroanatomical and neurophysiological foundation.