Matthew E. Taylor

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Washington State University ∙ Borealis AI

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

Deep reinforcement learning (deep RL) has achieved superior performance ...
12/21/2018 ∙ by Gabriel V. de la Cruz, et al. ∙ 46 ∙ share

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

How to best explore in domains with sparse, delayed, and deceptive rewar...
07/26/2019 ∙ by Chao Gao, et al. ∙ 5 ∙ share

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Interactive Learning of Environment Dynamics for Sequential Tasks

In order for robots and other artificial agents to efficiently learn to ...
07/19/2019 ∙ by Robert Loftin, et al. ∙ 5 ∙ share

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

Safe reinforcement learning has many variants and it is still an open re...
04/10/2019 ∙ by Bilal Kartal, et al. ∙ 1 ∙ share

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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 ...
09/14/2017 ∙ by Behzad Ghazanfari, et al. ∙ 0 ∙ share

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

Deep reinforcement learning (deep RL) has achieved superior performance ...
09/12/2017 ∙ by Gabriel V. de la Cruz Jr, et al. ∙ 0 ∙ share

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Learning to Teach Reinforcement Learning Agents

In this article we study the transfer learning model of action advice un...
07/28/2017 ∙ by Anestis Fachantidis, et al. ∙ 0 ∙ share

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Interactive Learning from Policy-Dependent Human Feedback

For agents and robots to become more useful, they must be able to quickl...
01/21/2017 ∙ by James MacGlashan, et al. ∙ 0 ∙ share

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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...
05/10/2018 ∙ by Kenny Young, et al. ∙ 0 ∙ share

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

Reinforcement learning has enjoyed multiple successes in recent years. H...
05/11/2018 ∙ by Zhaodong Wang, et al. ∙ 0 ∙ share

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

Reinforcement Learning (RL) can be extremely effective in solving comple...
05/15/2018 ∙ by Ariel Rosenfeld, et al. ∙ 0 ∙ share

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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...
11/17/2018 ∙ by Behzad Ghazanfari, et al. ∙ 0 ∙ share

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

Deep reinforcement learning (DRL) has achieved great successes in recent...
11/30/2018 ∙ by Bilal Kartal, et al. ∙ 0 ∙ share

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

Deep Reinforcement Learning (DRL) algorithms are known to be data ineffi...
04/03/2019 ∙ by Gabriel V. de la Cruz Jr, et al. ∙ 0 ∙ share

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

Deep reinforcement learning (DRL) has achieved outstanding results in re...
10/12/2018 ∙ by Pablo Hernandez-Leal, et al. ∙ 0 ∙ share

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

The Pommerman Team Environment is a recently proposed benchmark which in...
04/20/2019 ∙ by Chao Gao, et al. ∙ 0 ∙ share

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Action Guidance with MCTS for Deep Reinforcement Learning

Deep reinforcement learning has achieved great successes in recent years...
07/25/2019 ∙ by Bilal Kartal, et al. ∙ 0 ∙ share

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Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning

Deep reinforcement learning has achieved great successes in recent years...
07/24/2019 ∙ by Bilal Kartal, et al. ∙ 0 ∙ share

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Agent Modeling as Auxiliary Task for Deep Reinforcement Learning

In this paper we explore how actor-critic methods in deep reinforcement ...
07/22/2019 ∙ by Pablo Hernandez-Leal, et al. ∙ 0 ∙ share

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Matthew E. Taylor

Featured Co-authors

Chao Gao

Michael L. Littman

Fatemeh Afghah

Sarit Kraus

Pablo Hernandez-Leal

Bilal Kartal

Baoxiang Wang

Kenny Young

Ariel Rosenfeld

James MacGlashan

Yunshu Du

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

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

Interactive Learning of Environment Dynamics for Sequential Tasks

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

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

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

Learning to Teach Reinforcement Learning Agents

Interactive Learning from Policy-Dependent Human Feedback

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

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

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

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

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

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

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

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

Action Guidance with MCTS for Deep Reinforcement Learning

Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning

Agent Modeling as Auxiliary Task for Deep Reinforcement Learning