Advancing Deep Reinforcement Learning with NetHack, w/ Tim Rocktäschel - #527
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Understanding Computational Demands in Reinforcement Learning Research
This chapter explores the computational demands of training reinforcement learning agents in a high-efficiency environment. It highlights the contrast between the environment's speed and the resource-intensive nature of larger deep learning models, emphasizing the potential need for multiple GPUs depending on model complexity and research scope.
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