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07/19/2023
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay
Recommender systems are a ubiquitous feature of online platforms. Increa...
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02/06/2023
A Strong Baseline for Batch Imitation Learning
Imitation of expert behaviour is a highly desirable and safe approach to...
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08/10/2021
Imitation Learning by Reinforcement Learning
Imitation Learning algorithms learn a policy from demonstrations of expe...
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02/24/2021
Information Directed Reward Learning for Reinforcement Learning
For many reinforcement learning (RL) applications, specifying a reward i...
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01/22/2021
Estimating α-Rank by Maximizing Information Gain
Game theory has been increasingly applied in settings where the game is ...
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01/18/2021
Regularized Policies are Reward Robust
Entropic regularization of policies in Reinforcement Learning (RL) is a ...
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01/14/2021
Evaluating the Robustness of Collaborative Agents
In order for agents trained by deep reinforcement learning to work along...
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01/11/2021
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning
Agents that interact with other agents often do not know a priori what t...
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07/16/2020
DRIFT: Deep Reinforcement Learning for Functional Software Testing
Efficient software testing is essential for productive software developm...
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07/04/2020
Discount Factor as a Regularizer in Reinforcement Learning
Specifying a Reinforcement Learning (RL) task involves choosing a suitab...
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10/28/2019
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
The ability for policies to generalize to new environments is key to the...
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10/28/2019
Better Exploration with Optimistic Actor-Critic
Actor-critic methods, a type of model-free Reinforcement Learning, have ...
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09/25/2019
Pre-training as Batch Meta Reinforcement Learning with tiMe
Pre-training is transformative in supervised learning: a large network t...
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02/19/2018
Fourier Policy Gradients
We propose a new way of deriving policy gradient updates for reinforceme...
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01/10/2018
Expected Policy Gradients for Reinforcement Learning
We propose expected policy gradients (EPG), which unify stochastic polic...
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06/15/2017
Expected Policy Gradients
We propose expected policy gradients (EPG), which unify stochastic polic...
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05/24/2016
Alternating Optimisation and Quadrature for Robust Control
Bayesian optimisation has been successfully applied to a variety of rein...
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01/16/2015
Value Iteration with Options and State Aggregation
This paper presents a way of solving Markov Decision Processes that comb...
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01/22/2013
