Neural approaches to conversational AI
This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …
few years. We group conversational systems into three categories:(1) question answering …
Bellman-consistent pessimism for offline reinforcement learning
The use of pessimism, when reasoning about datasets lacking exhaustive exploration has
recently gained prominence in offline reinforcement learning. Despite the robustness it adds …
recently gained prominence in offline reinforcement learning. Despite the robustness it adds …
Bridging offline reinforcement learning and imitation learning: A tale of pessimism
Offline (or batch) reinforcement learning (RL) algorithms seek to learn an optimal policy from
a fixed dataset without active data collection. Based on the composition of the offline dataset …
a fixed dataset without active data collection. Based on the composition of the offline dataset …
The statistical complexity of interactive decision making
A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …
Dive into deep learning
This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …
teaching readers the concepts, the context, and the code. The entire book is drafted in …
Bilinear classes: A structural framework for provable generalization in rl
Abstract This work introduces Bilinear Classes, a new structural framework, which permit
generalization in reinforcement learning in a wide variety of settings through the use of …
generalization in reinforcement learning in a wide variety of settings through the use of …
Bellman eluder dimension: New rich classes of rl problems, and sample-efficient algorithms
Finding the minimal structural assumptions that empower sample-efficient learning is one of
the most important research directions in Reinforcement Learning (RL). This paper …
the most important research directions in Reinforcement Learning (RL). This paper …
Provably efficient reinforcement learning with linear function approximation
Abstract Modern Reinforcement Learning (RL) is commonly applied to practical problems
with an enormous number of states, where\emph {function approximation} must be deployed …
with an enormous number of states, where\emph {function approximation} must be deployed …
Nearly minimax optimal reinforcement learning for linear mixture markov decision processes
We study reinforcement learning (RL) with linear function approximation where the
underlying transition probability kernel of the Markov decision process (MDP) is a linear …
underlying transition probability kernel of the Markov decision process (MDP) is a linear …
When is partially observable reinforcement learning not scary?
Partial observability is ubiquitous in applications of Reinforcement Learning (RL), in which
agents learn to make a sequence of decisions despite lacking complete information about …
agents learn to make a sequence of decisions despite lacking complete information about …