AI Seminar Series 2023: Qingfeng Lan

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 March 24, Qingfeng Lan — PhD student at the University of Alberta — presented “Memory-efficient Reinforcement Learning with Knowledge Consolidation " at the AI Seminar.

Artificial neural networks are promising for general function approximation but challenging to train on non-independent or non-identically distributed data due to catastrophic forgetting. The experience replay buffer, a standard component in deep reinforcement learning, is often used to reduce forgetting and improve sample efficiency by storing experiences in a large buffer and using them for training later.

However, a large replay buffer results in a heavy memory burden, especially for onboard and edge devices with limited memory capacities. Lan proposes memory-efficient reinforcement learning algorithms based on the deep Q-network algorithm to alleviate this problem. The algorithms reduce forgetting and maintain high sample efficiency by consolidating knowledge from the target Q-network to the current Q-network.

Compared to baseline methods, these algorithms achieve comparable or better performance in both feature-based and image-based tasks while easing the burden of large experience replay buffers.

Watch the full presentation below:

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