Multilayer feedforward networks are universal approximators
Neural Networks
What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?
Neural Networks - Special issue on organisation of computation in brain-like systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiple model-based reinforcement learning
Neural Computation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Reinforcement Learning in Continuous Time and Space
Neural Computation
Reinforcement learning algorithm with CTRNN in continuous action space
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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We propose a reinforcement learning system designed to learn multiple different continuous state-action-space tasks. The system has been tested on a family of space-searching task akin to Morris water maze, but with obstacles. While exploring a task, the agent builds its internal model of the environment and approximates a state value function. For learning multiple tasks, we use a parametric bias switching mechanism in which the value of the parametric bias layer identifies the task for the agent. Each task has a specific parametric bias vector, and during training the vectors self-organize to reflect the structure of relationships between tasks in the task set. This mapping of the task set to parametric bias space can later be used to generate novel behaviors of the agent.