Same State, Different Task: Continual Reinforcement Learning without Interference

Continual Reinforcement Learning with Complex Synapses

Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgetting, whereby new learning can lead to abrupt erasure of previously acquired knowledge. Whereas in a neural network the parameters are typically modelled as scalar values, an individual synapse in the brain comprises a...

Reinforcement Learning without an Explicit Terminal State

| The article introduces a reinforcement learning framework based on dynamic programming for a class of control problems, where no explicit terminal state exists. This situation especially occurs in the context of technical process control: the control task is not terminated once a predeened target value is reached, but instead the controller has to continue to control the system in order to av...

Tuning Continual Exploration in Reinforcement Learning

This paper presents a model allowing to tune continual exploration in an optimal way by integrating exploration and exploitation in a common framework. It first quantifies exploration by defining the degree of exploration of a state as the entropy of the probability distribution for choosing an admissible action. Then, the exploration/exploitation tradeoff is formulated as a global optimization...

Task-Oriented Reinforcement Learning

Acknowledgement This thesis is the result of two years of work whereby I have been accompanied and supported by many people. I am extremely indebted to Dr.

Adaptive State-Space Quantisation and Multi-Task Reinforcement Learning Using . . .