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- 2reinforcement learning
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Fall 2024
Despite the brain's inherent ability to continually learn, biological insights are rarely applied to continual reinforcement learning (RL). This thesis addresses this gap by examining four under-investigated biologically-inspired modifications within the context of continual RL: energy...
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Fall 2020
Research in artificial general intelligence aims to create agents that can learn from their own experience to solve arbitrary tasks in complex and dynamic settings. To do so effectively and efficiently, such an agent must be able to predict how its environment will change both dependently and...
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Spring 2021
The backpropagation algorithm is a fundamental algorithm for training modern artificial neural networks (ANNs). However, it is known the backpropagation algorithm performs poorly on changing problems. We demonstrate the backpropagation algorithm can perform poorly on a clear, generic, changing...