Author

Henry Williams

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Conference Aug 2026

Uncertainty-Weighted Optimisation for Model-Based Reinforcement Learning

In Model-Based Reinforcement Learning, Deep Neural Networks are trained to be world models to simulate interactions between agents and their environments. Model biases are inevitable in such world models due to their limited size and the stochastic nature of training datasets. Instead of striving for a perfect world mo...

Ting Qiao, Henry Williams, Bruce A. MacDonald · 2026 IEEE International Conf... · 0 citations

#artificial intelligence Preprint Aug 2026

Integrating Novelty and Surprise for Experience Prioritization and Exploration in Image-Based Reinforcement Learning

Novelty and Surprise Prioritized Experience Replay (NSPER) is introduced, which uses novelty to capture underrepresented states and surprise to expose gaps in the agent's understanding of the environment and is extended with NSPER+R, integrating these signals as intrinsic rewards to jointly improve replay quality and e...

Hoda Yamani, Henry Williams, Bruce A. MacDonald · 0 citations