Search Results for author: Diana Borsa

Found 18 papers, 2 papers with code

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)

Selective Credit Assignment

no code implementations20 Feb 2022 Veronica Chelu, Diana Borsa, Doina Precup, Hado van Hasselt

Efficient credit assignment is essential for reinforcement learning algorithms in both prediction and control settings.

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

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.


Adapting Behaviour for Learning Progress

no code implementations14 Dec 2019 Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero

Determining what experience to generate to best facilitate learning (i. e. exploration) is one of the distinguishing features and open challenges in reinforcement learning.

Atari Games

The Termination Critic

no code implementations26 Feb 2019 Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Remi Munos, Doina Precup

In this work, we consider the problem of autonomously discovering behavioral abstractions, or options, for reinforcement learning agents.

Observational Learning by Reinforcement Learning

no code implementations20 Jun 2017 Diana Borsa, Bilal Piot, Rémi Munos, Olivier Pietquin

Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent.

reinforcement-learning Reinforcement Learning (RL)

Learning Shared Representations in Multi-task Reinforcement Learning

no code implementations7 Mar 2016 Diana Borsa, Thore Graepel, John Shawe-Taylor

We investigate a paradigm in multi-task reinforcement learning (MT-RL) in which an agent is placed in an environment and needs to learn to perform a series of tasks, within this space.

reinforcement-learning Reinforcement Learning (RL)

The Wreath Process: A totally generative model of geometric shape based on nested symmetries

no code implementations9 Jun 2015 Diana Borsa, Thore Graepel, Andrew Gordon

We consider the problem of modelling noisy but highly symmetric shapes that can be viewed as hierarchies of whole-part relationships in which higher level objects are composed of transformed collections of lower level objects.

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