Zap Stochastic Approximation and Implications to Q-Learning

Published on ● Video Link: https://www.youtube.com/watch?v=_3CNm-Fc828



Duration: 28:41
564 views
13


Sean Meyn (University of Florida)
https://simons.berkeley.edu/talks/tbd-244
Reinforcement Learning from Batch Data and Simulation




Other Videos By Simons Institute for the Theory of Computing


2020-12-04Policy Evaluation under Interference
2020-12-04Stable Reinforcement Learning with Unbounded State Space
2020-12-04Multiagent Reinforcement Learning: Rollout and Policy Iteration
2020-12-04Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation
2020-12-04Nearly Minimax Optimal Reward-Free Reinforcement Learning
2020-12-03Statistical Efficiency in Offline Reinforcement Learning
2020-12-03Batch Policy Learning in Average Reward Markov Decision Processes
2020-12-03Panel Discussion
2020-12-02The Mean-Squared Error of Double Q-Learning
2020-12-02Q-learning with Uniformly Bounded Variance
2020-12-02Zap Stochastic Approximation and Implications to Q-Learning
2020-12-02Computational/Statistical Gaps for Learning Neural Networks
2020-12-02Uniform Offline Policy Evaluation (OPE) and Offline Learning in Tabular RL
2020-12-02Batch Value-function Approximation with Only Realizability
2020-12-01Reinforcement Learning using Generative Models for Continuous State and Action Space Systems
2020-12-01Monte Carlo Sampling Approach to Solving Stochastic Multistage Programs
2020-12-01Robust Learning of Stochastic Dynamical Systems
2020-12-01Confident Off-policy Evaluation and Selection through Self-Normalized Importance Weighting
2020-12-01An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
2020-11-30Beyond Worst-Case: Instance-Dependent Optimality in Reinforcement Learning
2020-11-30Learning Multi-Agent Collaborations With Decomposition



Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Ana Busic
Reinforcement Learning from Batch Data and Simulation
Sean Meyn