AI Seminar Series 2022: Yi Wan

The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

On Feb. 23rd, Yi Wan – a Master's student in the Department of Computing Science at the University of Alberta – presented "Towards Adaptive Model-Based Reinforcement Learning" at the AI Seminar.

In recent years, a growing number of deep model-based reinforcement learning (RL) methods have been introduced. The interest in deep model-based RL is not surprising, given their many potential benefits, such as higher sample efficiency and the potential for fast adaption to changes in the environment. However, Wan demonstrates using an improved version of the recently introduced Local Change Adaptation (LoCA) setup, that the well-known model-based methods such as PlaNet and DreamerV2 perform poorly in their ability to adapt to local environmental changes. Combined with prior work that made a similar observation about the other popular model-based method, MuZero, a trend appears to emerge suggesting that current deep model-based methods have serious limitations. Wan dives deeper into the causes of this poor performance, by identifying elements that hurt adaptive behavior and linking these to underlying techniques frequently used in deep model-based RL. He empirically validates these insights in the case of linear function approximation by demonstrating that a modified version of linear Dyna achieves effective adaptation to local changes. Furthermore, Wan provides detailed insights into the challenges of building an adaptive non-linear model-based method, by experimenting with a non-linear version of Dyna.

Watch the full presentation below:

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