Framing reinforcement learning from human reward
W. Bradley Knox,Peter Stone +1 more
TLDR
The primary learning algorithm introduced in this article, which is called "vi-tamer", is the first algorithm to successfully learn non-myopically from reward generated by a human trainer and empirically shows that such non- myopic valuation facilitates higher-level understanding of the task.About:
This article is published in Artificial Intelligence.The article was published on 2015-08-01 and is currently open access. It has received 61 citations till now. The article focuses on the topics: Reward-based selection & Reinforcement learning.read more
Citations
More filters
Journal ArticleDOI
A Review of User Interface Design for Interactive Machine Learning
John Dudley,Per Ola Kristensson +1 more
TL;DR: A structural and behavioural model of a generalised IML system is proposed and a solution principles for building effective interfaces for IML are identified, identified strands of user interface research key to unlocking more efficient and productive non-expert interactive machine learning applications.
Posted Content
Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces
TL;DR: Deep TAMER is proposed, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer and demonstrated by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of Bowling.
Journal ArticleDOI
Human-Centered Reinforcement Learning: A Survey
TL;DR: The state-of-the-art human-centered RL algorithms are described and become a starting point for researchers who are initiating their endeavors in human- centered RL and references to the most interesting and successful works are provided.
Journal ArticleDOI
Social is special: A normative framework for teaching with and learning from evaluative feedback
TL;DR: It is suggested that human learning from evaluative feedback depends on inferences about communicative intent, goals and other mental states-much like learning from other sources, such as demonstration, observation and instruction.
Journal ArticleDOI
A Review on Interactive Reinforcement Learning From Human Social Feedback
TL;DR: Methods for interactive reinforcement learning agent to learn from human social feedback and the ways of delivering feedback are reviewed.
References
More filters
Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.
Journal ArticleDOI
A survey of robot learning from demonstration
TL;DR: A comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings, which analyzes and categorizes the multiple ways in which examples are gathered, as well as the various techniques for policy derivation.
Journal ArticleDOI
A survey of cross-validation procedures for model selection
Sylvain Arlot,Alain Celisse +1 more
TL;DR: This survey intends to relate the model selection performances of cross-validation procedures to the most recent advances of model selection theory, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results.
Related Papers (5)
Teachable robots: Understanding human teaching behavior to build more effective robot learners
Interactively shaping agents via human reinforcement: the TAMER framework
W. Bradley Knox,Peter Stone +1 more