according to Google researchers, the advancement of deep reinforcement learning comes at a cost--- a computational one.
Can a research done on a smaller computational budget can provide valuable scientific insights? Given the insane training times and budgets, it is natural to wonder if anything worthwhile in AI comes at a small price. So far, the researchers have focused on the training costs of language models which have become too large. But, what about the deep reinforcement learning(RL) algorithms -the brains behind autonomous cars, warehouse robots and even the AI that beat chess grandmasters?
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Deep Q-Networks have revolutionized the field of Deep Reinforcement Learning, but the technical prerequisites for easy experimentation have barred newcomers until now.
The Association of Data Scientists is holding a full-day workshop on building games using reinforcement learning on Saturday, February 20.
This paper presents a deep reinforcement learning model that learns control policies directly from high-dimensional sensory inputs.
Designing user experiences is a difficult art. Compared to other applications, video games provide designers a huge canvas to work with.