Reinforcement Learning, Fast and Slow
Offered By: Yannic Kilcher via YouTube
Course Description
Overview
Explore a comprehensive review of recent advancements in deep reinforcement learning (RL) and their implications for cognitive science. Delve into cutting-edge techniques that have significantly improved the sample efficiency of deep RL, challenging the notion that it is too slow to model human learning. Examine the fundamental connection between fast RL and slower, incremental forms of learning. Investigate how these AI methods could potentially revolutionize our understanding of psychology and neuroscience. Cover key topics including deep RL, memory-based approaches, meta-learning, and their potential applications in cognitive modeling.
Syllabus
Intro
Deep RL
Memory
Meta
Conclusion
Taught by
Yannic Kilcher
Related Courses
6.S094: Deep Learning for Self-Driving CarsMassachusetts Institute of Technology via Independent Natural Language Processing (NLP)
Microsoft via edX Deep Reinforcement Learning
Nvidia Deep Learning Institute via Udacity Advanced AI: Deep Reinforcement Learning in Python
Udemy Self-driving go-kart with Unity-ML
Udemy