b'Adam White'

research

∙ 07/10/2023

Measuring and Mitigating Interference in Reinforcement Learning

Catastrophic interference is common in many network-based learning syste...

0 Vincent Liu, et al. ∙

research

∙ 04/03/2023

Empirical Design in Reinforcement Learning

Empirical design in reinforcement learning is no small task. Running goo...

0 Andrew Patterson, et al. ∙

research

∙ 03/13/2023

Loss of Plasticity in Continual Deep Reinforcement Learning

The ability to learn continually is essential in a complex and changing ...

2 Zaheer Abbas, et al. ∙

research

∙ 02/28/2023

The In-Sample Softmax for Offline Reinforcement Learning

Reinforcement learning (RL) agents can leverage batches of previously co...

0 Chenjun Xiao, et al. ∙

research

∙ 11/15/2022

Agent-State Construction with Auxiliary Inputs

In many, if not every realistic sequential decision-making task, the dec...

0 Ruo Yu Tao, et al. ∙

research

∙ 10/25/2022

Auxiliary task discovery through generate-and-test

In this paper, we explore an approach to auxiliary task discovery in rei...

0 Banafsheh Rafiee, et al. ∙

research

∙ 06/06/2022

Goal-Space Planning with Subgoal Models

This paper investigates a new approach to model-based reinforcement lear...

11 Chunlok Lo, et al. ∙

research

∙ 05/18/2022

No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL

The performance of reinforcement learning (RL) agents is sensitive to th...

0 Han Wang, et al. ∙

research

∙ 04/01/2022

What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy

Auxiliary tasks have been argued to be useful for representation learnin...

0 Banafsheh Rafiee, et al. ∙

research

∙ 03/30/2022

Investigating the Properties of Neural Network Representations in Reinforcement Learning

In this paper we investigate the properties of representations learned b...

26 Han Wang, et al. ∙

research

∙ 03/17/2022

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

Learned communication between agents is a powerful tool when approaching...

0 Patrick M. Pilarski, et al. ∙

research

∙ 02/22/2022

Continual Auxiliary Task Learning

Learning auxiliary tasks, such as multiple predictions about the world, ...

0 Matthew McLeod, et al. ∙

research

∙ 02/07/2022

Reward-Respecting Subtasks for Model-Based Reinforcement Learning

To achieve the ambitious goals of artificial intelligence, reinforcement...

0 Richard S. Sutton, et al. ∙

research

∙ 01/11/2022

Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making

In this paper, we contribute a multi-faceted study into Pavlovian signal...

0 Andrew Butcher, et al. ∙

research

∙ 12/14/2021

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

Artificial intelligence systems increasingly involve continual learning ...

0 Dylan J. A. Brenneis, et al. ∙

research

∙ 09/20/2021

Counterfactual Instances Explain Little

In many applications, it is important to be able to explain the decision...

4 Adam White, et al. ∙

research

∙ 07/12/2021

Learning Expected Emphatic Traces for Deep RL

Off-policy sampling and experience replay are key for improving sample e...

0 Ray Jiang, et al. ∙

research

∙ 06/28/2021

Contrastive Counterfactual Visual Explanations With Overdetermination

A novel explainable AI method called CLEAR Image is introduced in this p...

13 Adam White, et al. ∙

research

∙ 06/21/2021

Emphatic Algorithms for Deep Reinforcement Learning

Off-policy learning allows us to learn about possible policies of behavi...

0 Ray Jiang, et al. ∙

research

∙ 04/28/2021

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning

Many reinforcement learning algorithms rely on value estimation. However...

0 Andrew Patterson, et al. ∙

research

∙ 07/07/2020

Towards a practical measure of interference for reinforcement learning

Catastrophic interference is common in many network-based learning syste...

28 Vincent Liu, et al. ∙

research

∙ 07/01/2020

Gradient Temporal-Difference Learning with Regularized Corrections

It is still common to use Q-learning and temporal difference (TD) learni...

3 Sina Ghiassian, et al. ∙

research

∙ 03/16/2020

Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks

Reinforcement learning systems require good representations to work well...

17 Sina Ghiassian, et al. ∙

research

∙ 08/08/2019

Measurable Counterfactual Local Explanations for Any Classifier

We propose a novel method for explaining the predictions of any classifi...

2 Adam White, et al. ∙

research

∙ 07/17/2019

Meta-descent for Online, Continual Prediction

This paper investigates different vector step-size adaptation approaches...

0 Andrew Jacobsen, et al. ∙

research

∙ 06/19/2019

Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study

Learning about many things can provide numerous benefits to a reinforcem...

2 Cam Linke, et al. ∙

research

∙ 04/02/2019

Planning with Expectation Models

Distribution and sample models are two popular model choices in model-ba...

20 Yi Wan, et al. ∙

research

∙ 11/16/2018

The Barbados 2018 List of Open Issues in Continual Learning

We want to make progress toward artificial general intelligence, namely ...

0 Tom Schaul, et al. ∙

research

∙ 11/15/2018

Context-Dependent Upper-Confidence Bounds for Directed Exploration

Directed exploration strategies for reinforcement learning are critical ...

6 Raksha Kumaraswamy, et al. ∙

research

∙ 11/06/2018

Online Off-policy Prediction

This paper investigates the problem of online prediction learning, where...

8 Sina Ghiassian, et al. ∙

research

∙ 07/18/2018

General Value Function Networks

In this paper we show that restricting the representation-layer of a Rec...

0 Matthew Schlegel, et al. ∙

research

∙ 06/12/2018

Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-based Planning in Continuous State Domains

Model-based strategies for control are critical to obtain sample efficie...

4 Yangchen Pan, et al. ∙

research

∙ 01/25/2018

Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods

This paper investigates estimating the variance of a temporal-difference...

0 Craig Sherstan, et al. ∙

research

∙ 11/28/2016

Accelerated Gradient Temporal Difference Learning

The family of temporal difference (TD) methods span a spectrum from comp...

0 Yangchen Pan, et al. ∙

research

∙ 07/02/2016

A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning

One of the main obstacles to broad application of reinforcement learning...

0 Martha White, et al. ∙

research

∙ 06/17/2016

Introspective Agents: Confidence Measures for General Value Functions

Agents of general intelligence deployed in real-world scenarios must ada...

0 Craig Sherstan, et al. ∙

research

∙ 02/28/2016

Investigating practical linear temporal difference learning

Off-policy reinforcement learning has many applications including: learn...

0 Adam White, et al. ∙

Adam White

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