Invariant Prediction for Generalization in Reinforcement Learning

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=FvQbrE3tyoE


Duration: 28:29
1,056 views
29

Clare Lyle (University of Oxford)
https://simons.berkeley.edu/talks/tbd-212
Deep Reinforcement Learning



Other Videos By Simons Institute for the Theory of Computing


2020-10-09Robustly Learning Mixtures of (Clusterable) Gaussians via the SoS Proofs to Algorithms Method
2020-10-05Richard M. Karp Distinguished Lecture – Safe Learning in Robotics
2020-10-02Deep Robust Reinforcement Learning and Regularization
2020-10-02Mixed Autonomy Traffic: A Reinforcement Learning Perspective
2020-10-02CoinDICE: Off-Policy Confidence Interval Estimation via Dual Lens
2020-10-02Rigorous Uncertainty Quantification for Off-policy Evaluation in Reinforcement Learning: a Variation
2020-10-02Is Safe Learning the Future of RL?
2020-10-01Policy Gradients Methods, Neural Policy Classes, and Distribution Shift
2020-10-01Fast Reinforcement Learning With Generalized Policy Updates
2020-10-01Exploiting Latent Structure and Bisimulation Metrics for Better Generalization
2020-10-01Invariant Prediction for Generalization in Reinforcement Learning
2020-10-01Language as a Scaffold for Reinforcement Learning
2020-09-30Discussion: Optimization
2020-09-30Generalizing the Projected Bellman Error Objective for Nonlinear Value Estimation
2020-09-30Stabilizing Q-learning with Weighted Bellman Losses
2020-09-30Munchausen Reinforcement Learning
2020-09-30Adaptive Approximate Policy Iteration
2020-09-30On Distance Approximation for Graph Properties
2020-09-29Discussion: Exploration
2020-09-29PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
2020-09-29Temporally-Extended ε-Greedy Exploration


Tags:
Clare Lyle
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Deep Reinforcement Learning