Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Self-Organizing Maps
Actor-critic models of the basal ganglia: new anatomical and computational perspectives
Neural Networks - Computational models of neuromodulation
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Cognitive Systems Research
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Simultaneously learning to recognize and control a low-cost robotic arm
Image and Vision Computing
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Modelling mental rotation in cognitive robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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When monkeys tackle novel complex behavioral tasks by trial-and-error they select actions from repertoires of sensorimotor primitives that allow them to search solutions in a space which is coarser than the space of fine movements Neuroscientific findings suggested that upper-limb sensorimotor primitives might be encoded, in terms of the final goal-postures they pursue, in premotor cortex A previous work by the authors reproduced these results in a model based on the idea that cortical pathways learn sensorimotor primitives while basal ganglia learn to assemble and trigger them to pursue complex reward-based goals This paper extends that model in several directions: a) it uses a Kohonen network to create a neural map with population encoding of postural primitives; b) it proposes an actor-critic reinforcement learning algorithm capable of learning to select those primitives in a biologically plausible fashion (i.e., through a dynamic competition between postures); c) it proposes a procedure to pre-train the actor to select promising primitives when tackling novel reinforcement learning tasks Some tests (obtained with a task used for studying monkeys engaged in learning reaching-action sequences) show that the model is computationally sound and capable of learning to select sensorimotor primitives from the postures' continuous space on the basis of their population encoding.