Towards Fast Autonomous Learners: Advances in Reinforcement Learning - 2015
Offered By: Center for Language & Speech Processing(CLSP), JHU via YouTube
Course Description
Overview
Syllabus
Intro
Markov Decision Process (MDP)
Reinforcement Learning
Unbiased Policy Evaluation for General RL in Short Horizons
Queue-based Offline Evaluation of Online Bandit Algorithms
Our Queue Approach Can Sometimes Evaluate Algorithms that Use Deterministic Policies for Many More Time Steps than Rejection
Sample Complexity of RL
Provably More Efficient Learners
Fast, Better Policy Search using Bayesian Optimization
Black Box Optimization
Opening the Box: Leverage Offline Policy Evaluation
Personalization & Transfer Learning for Sequential Decision Making Tasks
Latent Variable Modeling Background
Diameter Assumption: Needed for Sample Complexity Improvement in Transfer?
Active Set is Models Compatible with Current Task's Data
More Data Efficient Learning In Domains Where It Matters
Taught by
Center for Language & Speech Processing(CLSP), JHU
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