32 citations · 122 across the 16 of their papers we have counts for
18 papers
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning
Anthony GX-Chen, Ankit Anand, Gheorghe Comanici +7
Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward. Yet, modern applications such as language model fin…
Spectral Souping: A Unified Framework for Online Preference Alignment
Yinlam Chow, Guy Tennenholtz, Ted Yun +4
Reinforcement Learning from Human Feedback (RLHF) effectively aligns Large Language Models (LLMs) with aggregate human preferences but often fails to address the diverse and confli…
A Definition of Continual Reinforcement Learning
David Abel, André Barreto, Benjamin Van Roy +3
In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward. However, this perspective is based on…
The Option Keyboard: Combining Skills in Reinforcement Learning
André Barreto, Diana Borsa, Shaobo Hou +8
The ability to combine known skills to create new ones may be crucial in the solution of complex reinforcement learning problems that unfold over extended periods. We argue that a…
Discovering a set of policies for the worst case reward
Tom Zahavy, Andre Barreto, Daniel J Mankowitz +4
We study the problem of how to construct a set of policies that can be composed together to solve a collection of reinforcement learning tasks. Each task is a different reward func…
Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning
Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani
Network seeding for efficient information diffusion over time-varying graphs~(TVGs) is a challenging task with many real-world applications. There are several ways to model this sp…
The Value Equivalence Principle for Model-Based Reinforcement Learning
Christopher Grimm, André Barreto, Satinder Singh +1
Learning models of the environment from data is often viewed as an essential component to building intelligent reinforcement learning (RL) agents. The common practice is to separat…
Temporal Difference Uncertainties as a Signal for Exploration
Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann +7
An effective approach to exploration in reinforcement learning is to rely on an agent's uncertainty over the optimal policy, which can yield near-optimal exploration strategies in…
Expected Eligibility Traces
Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel +3
The question of how to determine which states and actions are responsible for a certain outcome is known as the credit assignment problem and remains a central research question in…
Temporally-Extended ε-Greedy Exploration
Will Dabney, Georg Ostrovski, André Barreto
Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expen…
Approximating Network Centrality Measures Using Node Embedding and Machine Learning
Matheus R. F. Mendonça, André M. S. Barreto, Artur Ziviani
Extracting information from real-world large networks is a key challenge nowadays. For instance, computing a node centrality may become unfeasible depending on the intended central…
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning
Will Dabney, André Barreto, Mark Rowland +4
In value-based reinforcement learning (RL), unlike in supervised learning, the agent faces not a single, stationary, approximation problem, but a sequence of value prediction probl…
Disentangled Cumulants Help Successor Representations Transfer to New Tasks
Christopher Grimm, Irina Higgins, Andre Barreto +5
Biological intelligence can learn to solve many diverse tasks in a data efficient manner by re-using basic knowledge and skills from one task to another. Furthermore, many of such…
General non-linear Bellman equations
Hado van Hasselt, John Quan, Matteo Hessel +3
We consider a general class of non-linear Bellman equations. These open up a design space of algorithms that have interesting properties, which has two potential advantages. First,…
Universal Successor Features Approximators
Diana Borsa, André Barreto, John Quan +5
The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks int…
Fast deep reinforcement learning using online adjustments from the past
Steven Hansen, Pablo Sprechmann, Alexander Pritzel +2
We propose Ephemeral Value Adjusments (EVA): a means of allowing deep reinforcement learning agents to rapidly adapt to experience in their replay buffer. EVA shifts the value pred…
Practical Kernel-Based Reinforcement Learning
André M. S. Barreto, Doina Precup, Joelle Pineau
Kernel-based reinforcement learning (KBRL) stands out among reinforcement learning algorithms for its strong theoretical guarantees. By casting the learning problem as a local kern…
Classification-based Approximate Policy Iteration: Experiments and Extended Discussions
Amir-massoud Farahmand, Doina Precup, André M. S. Barreto +1
Tackling large approximate dynamic programming or reinforcement learning problems requires methods that can exploit regularities, or intrinsic structure, of the problem in hand. Mo…