Matthew E. Taylor

Useful Policy Invariant Shaping from Arbitrary Advice

Reinforcement learning is a powerful learning paradigm in which agents c...
11/02/2020 ∙ by Paniz Behboudian, et al. ∙ 0 ∙ share

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Maximum Reward Formulation In Reinforcement Learning

Reinforcement learning (RL) algorithms typically deal with maximizing th...
10/08/2020 ∙ by Sai Krishna Gottipati, et al. ∙ 23 ∙ share

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

Experience replay (ER) improves the data efficiency of off-policy reinfo...
09/29/2020 ∙ by Yunshu Du, et al. ∙ 0 ∙ share

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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...
07/03/2020 ∙ by Adam Bignold, et al. ∙ 0 ∙ share

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Work in Progress: Temporally Extended Auxiliary Tasks

Predictive auxiliary tasks have been shown to improve performance in num...
04/01/2020 ∙ by Craig Sherstan, et al. ∙ 0 ∙ share

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Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

Reinforcement learning (RL) is a popular paradigm for addressing sequent...
03/10/2020 ∙ by Sanmit Narvekar, et al. ∙ 16 ∙ share

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Multi Type Mean Field Reinforcement Learning

Mean field theory provides an effective way of scaling multiagent reinfo...
02/06/2020 ∙ by Sriram Ganapathi Subramanian, et al. ∙ 16 ∙ 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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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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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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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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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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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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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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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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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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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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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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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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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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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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Matthew E. Taylor

Featured Co-authors

Peter Stone

Pascal Poupart

Chao Gao

Michael Bowling

Sarath Chandar

Fatemeh Afghah

Michael L. Littman

Garrett Warnell

Ahmed Touati

Matteo Leonetti

Francisco Cruz

Useful Policy Invariant Shaping from Arbitrary Advice

Maximum Reward Formulation In Reinforcement Learning

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

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

Work in Progress: Temporally Extended Auxiliary Tasks

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

Multi Type Mean Field Reinforcement Learning

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

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

Interactive Learning of Environment Dynamics for Sequential Tasks

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

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

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

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

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

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

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

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

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

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

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