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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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02/06/2023
Optimization using Parallel Gradient Evaluations on Multiple Parameters
We propose a first-order method for convex optimization, where instead o...
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01/24/2023
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments
Methods for sequential decision-making are often built upon a foundation...
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08/24/2022
Enforcing Delayed-Impact Fairness Guarantees
Recent research has shown that seemingly fair machine learning models, w...
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06/06/2022
Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL
Model-based reinforcement learning promises to learn an optimal policy f...
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12/10/2021
Edge-Compatible Reinforcement Learning for Recommendations
Most reinforcement learning (RL) recommendation systems designed for edg...
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11/06/2021
SOPE: Spectrum of Off-Policy Estimators
Many sequential decision making problems are high-stakes and require off...
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06/06/2021
Towards Practical Mean Bounds for Small Samples
Historically, to bound the mean for small sample sizes, practitioners ha...
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05/31/2021
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs
We study the problem of Safe Policy Improvement (SPI) under constraints ...
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04/26/2021
Universal Off-Policy Evaluation
When faced with sequential decision-making problems, it is often useful ...
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01/25/2021
High-Confidence Off-Policy (or Counterfactual) Variance Estimation
Many sequential decision-making systems leverage data collected using pr...
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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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09/15/2020
Reinforcement Learning for Strategic Recommendations
Strategic recommendations (SR) refer to the problem where an intelligent...
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06/30/2020
Evaluating the Performance of Reinforcement Learning Algorithms
Performance evaluations are critical for quantifying algorithmic advance...
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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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01/06/2020
Learning Reusable Options for Multi-Task Reinforcement Learning
Reinforcement learning (RL) has become an increasingly active area of re...
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10/15/2019
Reinforcement learning with spiking coagents
Neuroscientific theory suggests that dopaminergic neurons broadcast glob...
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06/17/2019
Is the Policy Gradient a Gradient?
The policy gradient theorem describes the gradient of the expected disco...
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06/06/2019
Classical Policy Gradient: Preserving Bellman's Principle of Optimality
We propose a new objective function for finite-horizon episodic Markov d...
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06/05/2019
Reinforcement Learning When All Actions are Not Always Available
The Markov decision process (MDP) formulation used to model many real-wo...
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06/05/2019
Lifelong Learning with a Changing Action Set
In many real-world sequential decision making problems, the number of av...
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05/15/2019
A New Confidence Interval for the Mean of a Bounded Random Variable
We present a new method for constructing a confidence interval for the m...
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02/15/2019
Asynchronous Coagent Networks: Stochastic Networks for Reinforcement Learning without Backpropagation or a Clock
In this paper we introduce a reinforcement learning (RL) approach for tr...
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02/15/2019
Reinforcement Learning Without Backpropagation or a Clock
In this paper we introduce a reinforcement learning (RL) approach for tr...
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02/03/2019
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning
In this paper we consider the problem of how a reinforcement learning ag...
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02/01/2019
Learning Action Representations for Reinforcement Learning
Most model-free reinforcement learning methods leverage state representa...
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02/01/2019
Privacy Preserving Off-Policy Evaluation
Many reinforcement learning applications involve the use of data that is...
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12/04/2018
Natural Option Critic
The recently proposed option-critic architecture Bacon et al. provide a ...
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08/17/2017
On Ensuring that Intelligent Machines Are Well-Behaved
Machine learning algorithms are everywhere, ranging from simple data ana...
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06/20/2017
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines
We show how an action-dependent baseline can be used by the policy gradi...
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06/12/2017
Data-Efficient Policy Evaluation Through Behavior Policy Search
We consider the task of evaluating a policy for a Markov decision proces...
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06/09/2017
Decoupling Learning Rules from Representations
In the artificial intelligence field, learning often corresponds to chan...
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11/10/2016
Importance Sampling with Unequal Support
Importance sampling is often used in machine learning when training and ...
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04/04/2016
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
In this paper we present a new way of predicting the performance of a re...
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12/30/2015
A Notation for Markov Decision Processes
This paper specifies a notation for Markov decision processes....
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12/15/2015