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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Exploring Reinforcement Learning Challenges
This chapter examines the speaker's transition from natural language processing to reinforcement learning, discussing the limitations of current methodologies in the context of static datasets and deterministic environments. It stresses the importance of generalization and the potential of procedurally generated games, like NetHack, in training agents to handle diverse real-world challenges. The conversation also covers the necessity for researchers to understand the assumptions of simulation environments, focusing on issues such as overfitting and the need for robust model training.
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