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07/10/2023
Measuring and Mitigating Interference in Reinforcement Learning
Catastrophic interference is common in many network-based learning syste...
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04/03/2023
Empirical Design in Reinforcement Learning
Empirical design in reinforcement learning is no small task. Running goo...
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03/13/2023
Loss of Plasticity in Continual Deep Reinforcement Learning
The ability to learn continually is essential in a complex and changing ...
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02/28/2023
The In-Sample Softmax for Offline Reinforcement Learning
Reinforcement learning (RL) agents can leverage batches of previously co...
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11/15/2022
Agent-State Construction with Auxiliary Inputs
In many, if not every realistic sequential decision-making task, the dec...
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10/25/2022
Auxiliary task discovery through generate-and-test
In this paper, we explore an approach to auxiliary task discovery in rei...
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06/06/2022
Goal-Space Planning with Subgoal Models
This paper investigates a new approach to model-based reinforcement lear...
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05/18/2022
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
The performance of reinforcement learning (RL) agents is sensitive to th...
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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...
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03/30/2022
Investigating the Properties of Neural Network Representations in Reinforcement Learning
In this paper we investigate the properties of representations learned b...
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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...
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02/22/2022
Continual Auxiliary Task Learning
Learning auxiliary tasks, such as multiple predictions about the world, ...
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02/07/2022
Reward-Respecting Subtasks for Model-Based Reinforcement Learning
To achieve the ambitious goals of artificial intelligence, reinforcement...
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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...
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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 ...
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09/20/2021
Counterfactual Instances Explain Little
In many applications, it is important to be able to explain the decision...
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07/12/2021
Learning Expected Emphatic Traces for Deep RL
Off-policy sampling and experience replay are key for improving sample e...
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06/28/2021
Contrastive Counterfactual Visual Explanations With Overdetermination
A novel explainable AI method called CLEAR Image is introduced in this p...
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06/21/2021
Emphatic Algorithms for Deep Reinforcement Learning
Off-policy learning allows us to learn about possible policies of behavi...
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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...
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07/07/2020
Towards a practical measure of interference for reinforcement learning
Catastrophic interference is common in many network-based learning syste...
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07/01/2020
Gradient Temporal-Difference Learning with Regularized Corrections
It is still common to use Q-learning and temporal difference (TD) learni...
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03/16/2020
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks
Reinforcement learning systems require good representations to work well...
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08/08/2019
Measurable Counterfactual Local Explanations for Any Classifier
We propose a novel method for explaining the predictions of any classifi...
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07/17/2019
Meta-descent for Online, Continual Prediction
This paper investigates different vector step-size adaptation approaches...
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06/19/2019
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study
Learning about many things can provide numerous benefits to a reinforcem...
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04/02/2019
Planning with Expectation Models
Distribution and sample models are two popular model choices in model-ba...
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11/16/2018
The Barbados 2018 List of Open Issues in Continual Learning
We want to make progress toward artificial general intelligence, namely ...
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11/15/2018
Context-Dependent Upper-Confidence Bounds for Directed Exploration
Directed exploration strategies for reinforcement learning are critical ...
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11/06/2018
Online Off-policy Prediction
This paper investigates the problem of online prediction learning, where...
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07/18/2018
General Value Function Networks
In this paper we show that restricting the representation-layer of a Rec...
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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...
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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...
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11/28/2016
Accelerated Gradient Temporal Difference Learning
The family of temporal difference (TD) methods span a spectrum from comp...
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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...
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06/17/2016
Introspective Agents: Confidence Measures for General Value Functions
Agents of general intelligence deployed in real-world scenarios must ada...
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02/28/2016