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Please note: This master’s thesis presentation will be given online.

Colin Vandenhof, Master’s candidate
David R. Cheriton School of Computer Science

Reinforcement learning (RL) is a powerful tool for developing intelligent agents, and the use of neural networks makes RL techniques more scalable to challenging real-world applications, from task-oriented dialogue systems to autonomous driving. However, one of the major bottlenecks to the adoption of RL is efficiency, as it often takes many time steps to learn an acceptable policy.