AI Rewind 2021: Trends in Reinforcement Learning with Kamyar Azizzadenesheli
EPISODE 560
|
FEBRUARY
21,
2022
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About this Episode
Today we’re joined by Kamyar Azizzadenesheli, an assistant professor at Purdue University, to close out our AI Rewind 2021 series! In this conversation, we focused on all things deep reinforcement learning, starting with a general overview of the direction of the field, and though it might seem to be slowing, that's just a product of the light being shined constantly on the CV and NLP spaces. We dig into themes like the convergence of RL methodology with both robotics and control theory, as well as a few trends that Kamyar sees over the horizon, such as self-supervised learning approaches in RL. We also talk through Kamyar’s predictions for RL in 2022 and beyond. This was a fun conversation, and I encourage you to look through all the great papers and videos that Kamyar shared below!
About the Guest
Kamyar Azizzadenesheli
NVIDIA
Resources
- L4DC 2022: Learning for Dynamics and Control Conference 2022
- Paper: Improper Learning for Non-Stochastic Control
- Paper: Certainty Equivalence is Efficient for Linear Quadratic Control
- Paper: Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems
- Paper: Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy
- Paper: Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
- Paper: A Unified Off-Policy Evaluation Approach for General Value Function
- Paper: Off-Policy Risk Assessment in Contextual Bandits
- Paper: Universal Off-Policy Evaluation
- Paper: Learning High-Speed Flight in the Wild
- Learning High-Speed Flight in the Wild (Science Robotics, 2021)
- Paper: Learning Quadrupedal Locomotion over Challenging Terrain
- Paper: Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions
- Paper: Provably Efficient Reinforcement Learning with Linear Function Approximation
- Paper: Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
- Paper: Bilinear Classes: A Structural Framework for Provable Generalization in RL
- Paper: Model-Based Reinforcement Learning with Value-Targeted Regression
- Paper: FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
- Article: YouTube's recommendations still push harmful videos, crowdsourced study finds
- Reinforcement Learning for Industrial AI with Pieter Abbeel - #476
- Deep Reinforcement Learning Primer and Research Frontiers with Kamyar Azizzadenesheli - #177
- Office Hours: Reinforcement Learning - The TWIML AI Podcast
- Trends in Reinforcement Learning with Pablo Samuel Castro - #443
- Trends in Reinforcement Learning with Chelsea Finn - #335
- Trends in Reinforcement Learning with Simon Osindero - TWIML Talk #217

