Search Results for author: André Barreto

Found 19 papers, 5 papers with code

A Distributional Analogue to the Successor Representation

1 code implementation13 Feb 2024 Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland

This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process.

Distributional Reinforcement Learning Model-based Reinforcement Learning +1

On the Convergence of Bounded Agents

no code implementations20 Jul 2023 David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh

Standard models of the reinforcement learning problem give rise to a straightforward definition of convergence: An agent converges when its behavior or performance in each environment state stops changing.


A Definition of Continual Reinforcement Learning

no code implementations NeurIPS 2023 David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh

Using this new language, we define a continual learning agent as one that can be understood as carrying out an implicit search process indefinitely, and continual reinforcement learning as the setting in which the best agents are all continual learning agents.

Continual Learning reinforcement-learning

Generalised Policy Improvement with Geometric Policy Composition

no code implementations17 Jun 2022 Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto

We introduce a method for policy improvement that interpolates between the greedy approach of value-based reinforcement learning (RL) and the full planning approach typical of model-based RL.

Continuous Control Reinforcement Learning (RL)

The Phenomenon of Policy Churn

no code implementations1 Jun 2022 Tom Schaul, André Barreto, John Quan, Georg Ostrovski

We identify and study the phenomenon of policy churn, that is, the rapid change of the greedy policy in value-based reinforcement learning.

reinforcement-learning Reinforcement Learning (RL)

Model-Value Inconsistency as a Signal for Epistemic Uncertainty

no code implementations8 Dec 2021 Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram Friesen, Feryal Behbahani, Tom Schaul, André Barreto, Simon Osindero

Unlike prior work which estimates uncertainty by training an ensemble of many models and/or value functions, this approach requires only the single model and value function which are already being learned in most model-based reinforcement learning algorithms.

Model-based Reinforcement Learning Rolling Shutter Correction

The Option Keyboard: Combining Skills in Reinforcement Learning

no code implementations NeurIPS 2019 André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan Hunt, Shibl Mourad, David Silver, Doina Precup

Building on this insight and on previous results on transfer learning, we show how to approximate options whose cumulants are linear combinations of the cumulants of known options.

Management reinforcement-learning +2

Proper Value Equivalence

1 code implementation NeurIPS 2021 Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh

The value-equivalence (VE) principle proposes a simple answer to this question: a model should capture the aspects of the environment that are relevant for value-based planning.

Model-based Reinforcement Learning Reinforcement Learning (RL)

Risk-Aware Transfer in Reinforcement Learning using Successor Features

no code implementations NeurIPS 2021 Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee

Sample efficiency and risk-awareness are central to the development of practical reinforcement learning (RL) for complex decision-making.

Decision Making reinforcement-learning +2

The Value Equivalence Principle for Model-Based Reinforcement Learning

no code implementations NeurIPS 2020 Christopher Grimm, André Barreto, Satinder Singh, David Silver

As our main contribution, we introduce the principle of value equivalence: two models are value equivalent with respect to a set of functions and policies if they yield the same Bellman updates.

Model-based Reinforcement Learning reinforcement-learning +2

Expected Eligibility Traces

no code implementations3 Jul 2020 Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa

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 reinforcement learning and artificial intelligence.


Temporally-Extended ε-Greedy Exploration

no code implementations ICLR 2021 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.

Reinforcement Learning (RL)

Fast deep reinforcement learning using online adjustments from the past

2 code implementations NeurIPS 2018 Steven Hansen, Pablo Sprechmann, Alexander Pritzel, André Barreto, Charles Blundell

We propose Ephemeral Value Adjusments (EVA): a means of allowing deep reinforcement learning agents to rapidly adapt to experience in their replay buffer.

Atari Games reinforcement-learning +2

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