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About the position
This project aims at setting an explainable foundation for developing lifelong-learning intelligent agent, where an agent is trained to master a sequence of sequential decision-making tasks over its lifetime (referred to as continual reinforcement learning (Continual-RL)). To emulate the remarkable lifelong learning ability of human beings, the agent is anticipated to acquire new tasks by leveraging knowledge from previous ones, and to enhance its proficiency in older tasks through insights gained from newer ones. Continual learning often suffers from the so-called catastrophic forgetting, i.e., the agent easily forgets the knowledge of old tasks when learning new tasks.
Existing approaches lack a theoretical understanding of how system parameters/setups, e.g., task similarity, task ordering, algorithm design, affect catastrophic forgetting and generalization, rely on heuristics to avoid task conflicting, and are vulnerable to model mismatch. At this juncture a post-doctoral researcher is sought to work on novel theory, algorithm and applications of continual reinforcement learning.
Preferred candidates shall have a strong background in reinforcement learning and deep learning theory, have a track record of publication at top machine learning conferences and journals.
How to apply
Those interested in this position are encouraged to contact Professor Shaofeng Zou in the School of Electrical, Computer and Energy Engineering, Arizona State University: email: Zou@asu.edu. More details about Prof. Zou can be found here: https://sites.google.com/view/szou/home
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