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Recent Advances in Deep Reinforcement Learning | AI Talks
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1W-Minds:Dec 1,Zhaoran Wang: Demystifying (Deep) Reinforcement Learning with Optimism and Pessimism
Emma Brunskill (Stanford University): "Efficient Reinforcement Learning When Data is Costly"
RLOO: A Cost-Efficient Optimization for Learning from Human Feedback in LLMs
Sham Kakade: Representation, Modeling, and Optimization in Reinforcement Learning
Adaptive Discretization For Reinforcement Learning
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Towards Generalization and Efficiency in Reinforcement Learning
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RLHF: Reinforcement Learning with Once-per-Episode Feedback
RLSS 2023 - Temporal Difference Methods with Function Approximation - Herke van Hoof
Safe Reinforcement Learning in the Presence of Non-stationarity: Theory and Algorithms
ICML 2020 Oral Talk: Planning to Explore via Self-Supervised World Models
10701 Lecture 24 Reinforcement Learning