Publications by authors named "Robin Schiewer"
Hierarchical model-based reinforcement learning (HMBRL) aims to combine the sample efficiency of model-based reinforcement learning with the abstraction capability of hierarchical reinforcement learning. While HMBRL has great potential, the structural and conceptual complexities of current approaches make it challenging to extract general principles, hindering understanding and adaptation to new use cases, and thereby impeding the overall progress of the field. In this work we describe a novel HMBRL framework and evaluate it thoroughly.
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- The study highlights the issues caused by the derivative of activation functions in artificial neural networks, especially during continual learning, and introduces a new approach called Hebbian descent to address these problems.
- Hebbian descent uses an alternative loss function that ignores the derivative of the activation function, which helps prevent vanishing error signals in both shallow and deep networks, enhancing training effectiveness.
- By integrating centering with Hebbian descent, the method not only improves continual learning by reducing catastrophic interference but also shows performance on par with regular gradient descent in specific scenarios.