Matthew E. Taylor

The Atari Data Scraper

Reinforcement learning has made great strides in recent years due to the...
04/11/2021 ∙ by Brittany Davis Pierson, et al. ∙ 0 ∙ share

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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...
03/07/2021 ∙ by Volodymyr Tkachuk, et al. ∙ 0 ∙ share

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Model-Invariant State Abstractions for Model-Based Reinforcement Learning

Accuracy and generalization of dynamics models is key to the success of ...
02/19/2021 ∙ by Manan Tomar, et al. ∙ 2 ∙ share

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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...
02/02/2021 ∙ by Matthew E. Taylor, et al. ∙ 0 ∙ share

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

Cooperative multi-agent reinforcement learning (MARL) has achieved signi...
01/18/2021 ∙ by Nikunj Gupta, et al. ∙ 0 ∙ share

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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

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