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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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05/16/2023
Coagent Networks: Generalized and Scaled
Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011...
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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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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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02/23/2023
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments
In this work, we consider the off-policy policy evaluation problem for c...
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01/27/2023
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
Many policy optimization approaches in reinforcement learning incorporat...
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01/26/2023
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
Off-policy learning from multistep returns is crucial for sample-efficie...
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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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05/17/2022
Robust Losses for Learning Value Functions
Most value function learning algorithms in reinforcement learning are ba...
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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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03/22/2022
Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum
Most convergence guarantees for stochastic gradient descent with momentu...
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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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02/04/2022
A Temporal-Difference Approach to Policy Gradient Estimation
The policy gradient theorem (Sutton et al., 2000) prescribes the usage o...
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12/22/2021
An Alternate Policy Gradient Estimator for Softmax Policies
Policy gradient (PG) estimators for softmax policies are ineffective wit...
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11/16/2021
Off-Policy Actor-Critic with Emphatic Weightings
A variety of theoretically-sound policy gradient algorithms exist for th...
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11/15/2021
Exploiting Action Impact Regularity and Partially Known Models for Offline Reinforcement Learning
Offline reinforcement learning-learning a policy from a batch of data-is...
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07/17/2021
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Approximate Policy Iteration (API) algorithms alternate between (approxi...
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05/29/2021
Predictive Representation Learning for Language Modeling
To effectively perform the task of next-word prediction, long short-term...
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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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03/09/2021
Scalable Online Recurrent Learning Using Columnar Neural Networks
Structural credit assignment for recurrent learning is challenging. An a...
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12/07/2020
Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
This report presents the debates, posters, and discussions of the Sim2Re...
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10/23/2020
Towards Safe Policy Improvement for Non-Stationary MDPs
Many real-world sequential decision-making problems involve critical sys...
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10/14/2020
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?
The representations generated by many models of language (word embedding...
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07/19/2020
Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities
Model-based reinforcement learning (MBRL) can significantly improve samp...
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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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07/05/2020
Selective Dyna-style Planning Under Limited Model Capacity
In model-based reinforcement learning, planning with an imperfect model ...
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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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06/12/2020
Learning Causal Models Online
Predictive models – learned from observational data not covering the com...
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06/08/2020
Hallucinating Value: A Pitfall of Dyna-style Planning with Imperfect Environment Models
Dyna-style reinforcement learning (RL) agents improve sample efficiency ...
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05/17/2020
Optimizing for the Future in Non-Stationary MDPs
Most reinforcement learning methods are based upon the key assumption th...
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05/11/2020
Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Information gathering in a partially observable environment can be formu...
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02/16/2020
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
Q-learning suffers from overestimation bias, because it approximates the...
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02/14/2020
An implicit function learning approach for parametric modal regression
For multi-valued functions—such as when the conditional distribution on ...
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10/03/2019
Is Fast Adaptation All You Need?
Gradient-based meta-learning has proven to be highly effective at learni...
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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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06/18/2019
Hill Climbing on Value Estimates for Search-control in Dyna
Dyna is an architecture for model-based reinforcement learning (RL), whe...
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06/11/2019
Importance Resampling for Off-policy Prediction
Importance sampling (IS) is a common reweighting strategy for off-policy...
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05/29/2019
Meta-Learning Representations for Continual Learning
A continual learning agent should be able to build on top of existing kn...
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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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12/03/2018
Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling
Knowledge distillation is an effective technique that transfers knowledg...
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11/22/2018
An Off-policy Policy Gradient Theorem Using Emphatic Weightings
Policy gradient methods are widely used for control in reinforcement lea...
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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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11/15/2018
The Utility of Sparse Representations for Control in Reinforcement Learning
We investigate sparse representations for control in reinforcement learn...
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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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10/22/2018
Actor-Expert: A Framework for using Action-Value Methods in Continuous Action Spaces
Value-based approaches can be difficult to use in continuous action spac...
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08/28/2018
High-confidence error estimates for learned value functions
Estimating the value function for a fixed policy is a fundamental proble...
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07/18/2018
