Adam White

Affiliations:
  • DeepMind Ltd, Edmonton, AB, Canada
  • Indiana University at Bloomington, Department of Computer Science, IN, USA
  • University of Alberta, Department of Computing Science, Edmonton, AB, Canada (PhD 2015)


According to our database1, Adam White authored at least 65 papers between 2006 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Links

Online presence:

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Bibliography

2024
GVFs in the real world: making predictions online for water treatment.
Mach. Learn., July, 2024

Real-Time Recurrent Learning using Trace Units in Reinforcement Learning.
CoRR, 2024

The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning.
CoRR, 2024

A New View on Planning in Online Reinforcement Learning.
CoRR, 2024

Tuning for the Unknown: Revisiting Evaluation Strategies for Lifelong RL.
CoRR, 2024

Investigating the properties of neural network representations in reinforcement learning.
Artif. Intell., 2024

Cross-environment Hyperparameter Tuning for Reinforcement Learning.
Proceedings of the 1st Reinforcement Learning Conference, 2024

Investigating the Interplay of Prioritized Replay and Generalization.
Proceedings of the 1st Reinforcement Learning Conference, 2024

Harnessing Discrete Representations for Continual Reinforcement Learning.
Proceedings of the 1st Reinforcement Learning Conference, 2024

The Cliff of Overcommitment with Policy Gradient Step Sizes.
Proceedings of the 1st Reinforcement Learning Conference, 2024

Position: Benchmarking is Limited in Reinforcement Learning Research.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint).
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Reward-respecting subtasks for model-based reinforcement learning.
Artif. Intell., November, 2023

From eye-blinks to state construction: Diagnostic benchmarks for online representation learning.
Adapt. Behav., February, 2023

Agent-State Construction with Auxiliary Inputs.
Trans. Mach. Learn. Res., 2023

Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning.
Trans. Mach. Learn. Res., 2023

Recurrent Linear Transformers.
CoRR, 2023

Empirical Design in Reinforcement Learning.
CoRR, 2023

The In-Sample Softmax for Offline Reinforcement Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Auxiliary task discovery through generate-and-test.
Proceedings of the Conference on Lifelong Learning Agents, 2023

Measuring and Mitigating Interference in Reinforcement Learning.
Proceedings of the Conference on Lifelong Learning Agents, 2023

Loss of Plasticity in Continual Deep Reinforcement Learning.
Proceedings of the Conference on Lifelong Learning Agents, 2023

Entropy as a Measure of Puzzle Difficulty.
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2023

2022
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL.
Trans. Mach. Learn. Res., 2022

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning.
J. Mach. Learn. Res., 2022

Goal-Space Planning with Subgoal Models.
CoRR, 2022

What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy.
CoRR, 2022

The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents.
CoRR, 2022

Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making.
CoRR, 2022

Learning Expected Emphatic Traces for Deep RL.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
General Value Function Networks.
J. Artif. Intell. Res., 2021

Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study.
CoRR, 2021

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning.
CoRR, 2021

Continual Auxiliary Task Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Emphatic Algorithms for Deep Reinforcement Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study.
J. Artif. Intell. Res., 2020

Towards a practical measure of interference for reinforcement learning.
CoRR, 2020

Gradient Temporal-Difference Learning with Regularized Corrections.
Proceedings of the 37th International Conference on Machine Learning, 2020

Training Recurrent Neural Networks Online by Learning Explicit State Variables.
Proceedings of the 8th International Conference on Learning Representations, 2020

Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study.
CoRR, 2019

Planning with Expectation Models.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

Meta-Descent for Online, Continual Prediction.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
The Barbados 2018 List of Open Issues in Continual Learning.
CoRR, 2018

Online Off-policy Prediction.
CoRR, 2018

General Value Function Networks.
CoRR, 2018

Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods.
CoRR, 2018

Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Context-dependent upper-confidence bounds for directed exploration.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017
GQ($λ$) Quick Reference and Implementation Guide.
CoRR, 2017

Accelerated Gradient Temporal Difference Learning.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning.
Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016

Investigating Practical Linear Temporal Difference Learning.
Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016

Introspective Agents: Confidence Measures for General Value Functions.
Proceedings of the Artificial General Intelligence - 9th International Conference, 2016

2014
Multi-timescale nexting in a reinforcement learning robot.
Adapt. Behav., 2014

2012
Acquiring a broad range of empirical knowledge in real time by temporal-difference learning.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2012

Scaling life-long off-policy learning.
Proceedings of the 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics, 2012

2011
Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction.
Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2011

2010
Report on the 2008 Reinforcement Learning Competition.
AI Mag., 2010

Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009
RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments.
J. Mach. Learn. Res., 2009

2006
Feature Construction for Reinforcement Learning in Hearts.
Proceedings of the Computers and Games, 5th International Conference, 2006


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