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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 3rd, Yan Wang— Master's student at the University of Alberta — presented “Effective Real-time Reinforcement Learning for Vision-Based Robotic Tasks" at the AI Seminar.
Recently we have seen many successful applications of Reinforcement Learning (RL). It is natural to extend the scope of RL to vision-based real-time learning of robotic control tasks. However, a vision-based real-time robotic RL agent faces some practical issues oft-ignored in conventional RL research. The first issue Yang investigates is that robots deployed in the real world are usually tethered to a resource-limited computer, while vision-based RL algorithms are expensive. The second issue he investigates is how to design an effective reward function that is independent of domain knowledge.
Watch the full presentation below:
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On October 27, Amii & University of Alberta PhD student Gautham Vasan presented "Learning Sparse-Reward Tasks on Real Robots From Scratch" at the AI Seminar.
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