Matthew E. Taylor

research

∙ 07/10/2023

Can You Improve My Code? Optimizing Programs with Local Search

This paper introduces a local search method for improving an existing pr...

0 Fatemeh Abdollahi, et al. ∙

research

∙ 02/13/2023

Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning

Tomorrow's robots will need to distinguish useful information from noise...

6 Bram Grooten, et al. ∙

research

∙ 11/14/2022

NeurIPS 2022 Competition: Driving SMARTS

Driving SMARTS is a regular competition designed to tackle problems caus...

0 Amir Rasouli, et al. ∙

research

∙ 10/13/2022

Augmenting Flight Training with AI to Efficiently Train Pilots

We propose an AI-based pilot trainer to help students learn how to fly a...

0 Michael Guevarra, et al. ∙

research

∙ 04/25/2022

Reinforcement Teaching

We propose Reinforcement Teaching: a framework for meta-learning in whic...

10 Alex Lewandowski, et al. ∙

research

∙ 04/14/2022

Methodical Advice Collection and Reuse in Deep Reinforcement Learning

Reinforcement learning (RL) has shown great success in solving many chal...

19 Sahir, et al. ∙

research

∙ 03/16/2022

PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration

Learning to collaborate is critical in Multi-Agent Reinforcement Learnin...

0 Pengyi Li, et al. ∙

research

∙ 11/15/2021

Learning Representations for Pixel-based Control: What Matters and Why?

Learning representations for pixel-based control has garnered significan...

0 Manan Tomar, et al. ∙

research

∙ 04/11/2021

The Atari Data Scraper

Reinforcement learning has made great strides in recent years due to the...

0 Brittany Davis Pierson, et al. ∙

research

∙ 03/07/2021

The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning

Some reinforcement learning methods suffer from high sample complexity c...

0 Volodymyr Tkachuk, et al. ∙

research

∙ 02/19/2021

Model-Invariant State Abstractions for Model-Based Reinforcement Learning

Accuracy and generalization of dynamics models is key to the success of ...

2 Manan Tomar, et al. ∙

research

∙ 02/02/2021

Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym

Reinforcement learning (RL) is a popular machine learning paradigm for g...

0 Matthew E. Taylor, et al. ∙

research

∙ 01/18/2021

HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging

Cooperative multi-agent reinforcement learning (MARL) has achieved signi...

0 Nikunj Gupta, et al. ∙

research

∙ 11/02/2020

Useful Policy Invariant Shaping from Arbitrary Advice

Reinforcement learning is a powerful learning paradigm in which agents c...

0 Paniz Behboudian, et al. ∙

research

∙ 10/08/2020

Maximum Reward Formulation In Reinforcement Learning

Reinforcement learning (RL) algorithms typically deal with maximizing th...

23 Sai Krishna Gottipati, et al. ∙

research

∙ 09/29/2020

Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy

Experience replay (ER) improves the data efficiency of off-policy reinfo...

0 Yunshu Du, et al. ∙

research

∙ 07/03/2020

A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review

A long-term goal of reinforcement learning agents is to be able to perfo...

0 Adam Bignold, et al. ∙

research

∙ 04/01/2020

Work in Progress: Temporally Extended Auxiliary Tasks

Predictive auxiliary tasks have been shown to improve performance in num...

0 Craig Sherstan, et al. ∙

research

∙ 03/10/2020

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

Reinforcement learning (RL) is a popular paradigm for addressing sequent...

16 Sanmit Narvekar, et al. ∙

research

∙ 02/06/2020

Multi Type Mean Field Reinforcement Learning

Mean field theory provides an effective way of scaling multiagent reinfo...

16 Sriram Ganapathi Subramanian, et al. ∙

research

∙ 07/26/2019

On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman

How to best explore in domains with sparse, delayed, and deceptive rewar...

5 Chao Gao, et al. ∙

research

∙ 07/25/2019

Action Guidance with MCTS for Deep Reinforcement Learning

Deep reinforcement learning has achieved great successes in recent years...

0 Bilal Kartal, et al. ∙

research

∙ 07/24/2019

Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning

Deep reinforcement learning has achieved great successes in recent years...

0 Bilal Kartal, et al. ∙

research

∙ 07/22/2019

Agent Modeling as Auxiliary Task for Deep Reinforcement Learning

In this paper we explore how actor-critic methods in deep reinforcement ...

0 Pablo Hernandez-Leal, et al. ∙

research

∙ 07/19/2019

Interactive Learning of Environment Dynamics for Sequential Tasks

In order for robots and other artificial agents to efficiently learn to ...

5 Robert Loftin, et al. ∙

research

∙ 04/20/2019

Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition

The Pommerman Team Environment is a recently proposed benchmark which in...

0 Chao Gao, et al. ∙

research

∙ 04/10/2019

Safer Deep RL with Shallow MCTS: A Case Study in Pommerman

Safe reinforcement learning has many variants and it is still an open re...

1 Bilal Kartal, et al. ∙

research

∙ 04/03/2019

Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) algorithms are known to be data ineffi...

0 Gabriel V. de la Cruz Jr, et al. ∙

research

∙ 12/21/2018

Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning

Deep reinforcement learning (deep RL) has achieved superior performance ...

46 Gabriel V. de la Cruz, et al. ∙

research

∙ 11/30/2018

Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL

Deep reinforcement learning (DRL) has achieved great successes in recent...

0 Bilal Kartal, et al. ∙

research

∙ 11/17/2018

Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach

Reinforcement learning (RL) techniques, while often powerful, can suffer...

0 Behzad Ghazanfari, et al. ∙

research

∙ 10/12/2018

Is multiagent deep reinforcement learning the answer or the question? A brief survey

Deep reinforcement learning (DRL) has achieved outstanding results in re...

0 Pablo Hernandez-Leal, et al. ∙

research

∙ 05/15/2018

Leveraging human knowledge in tabular reinforcement learning: A study of human subjects

Reinforcement Learning (RL) can be extremely effective in solving comple...

0 Ariel Rosenfeld, et al. ∙

research

∙ 05/11/2018

Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration

Reinforcement learning has enjoyed multiple successes in recent years. H...

0 Zhaodong Wang, et al. ∙

research

∙ 05/10/2018

Metatrace: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control

Reinforcement learning (RL) has had many successes in both "deep" and "s...

0 Kenny Young, et al. ∙

research

∙ 09/14/2017

Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning

Reinforcement learning (RL), while often powerful, can suffer from slow ...

0 Behzad Ghazanfari, et al. ∙

research

∙ 09/12/2017

Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning

Deep reinforcement learning (deep RL) has achieved superior performance ...

0 Gabriel V. de la Cruz Jr, et al. ∙

research

∙ 07/28/2017

Learning to Teach Reinforcement Learning Agents

In this article we study the transfer learning model of action advice un...

0 Anestis Fachantidis, et al. ∙

research

∙ 01/21/2017

Interactive Learning from Policy-Dependent Human Feedback

For agents and robots to become more useful, they must be able to quickl...

0 James MacGlashan, et al. ∙

Matthew E. Taylor

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