Temporal Effects in Actions
The discussion highlights how actions taken today can have long-term consequences, making the dynamics of reinforcement learning both challenging and fascinating. The complexities of exploring a vast environment and understanding the outcomes of various actions are emphasized, drawing parallels to everyday decisions, like grabbing coffee or investing in a bank. This interplay of immediate choices and their future impacts is central to understanding real-world scenarios in reinforcement learning.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Deep Reinforcement Learning Primer and Research Frontiers with Kamyar Azizzadenesheli - TWiML...
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