Diana Borsa | DeepAI

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∙ 09/07/2023

A State Representation for Diminishing Rewards

A common setting in multitask reinforcement learning (RL) demands that a...

0 Ted Moskovitz, et al. ∙

∙ 02/20/2022

Selective Credit Assignment

Efficient credit assignment is essential for reinforcement learning algo...

0 Veronica Chelu, et al. ∙

∙ 08/26/2021

When should agents explore?

Exploration remains a central challenge for reinforcement learning (RL)....

0 Miruna Pislar, et al. ∙

∙ 05/11/2021

Return-based Scaling: Yet Another Normalisation Trick for Deep RL

Scaling issues are mundane yet irritating for practitioners of reinforce...

0 Tom Schaul, et al. ∙

∙ 12/14/2019

Adapting Behaviour for Learning Progress

Determining what experience to generate to best facilitate learning (i.e...

18 Tom Schaul, et al. ∙

∙ 10/16/2019

Conditional Importance Sampling for Off-Policy Learning

The principal contribution of this paper is a conceptual framework for o...

12 Mark Rowland, et al. ∙

∙ 07/08/2019

General non-linear Bellman equations

We consider a general class of non-linear Bellman equations. These open ...

5 Hado van Hasselt, et al. ∙

∙ 04/25/2019

Ray Interference: a Source of Plateaus in Deep Reinforcement Learning

Rather than proposing a new method, this paper investigates an issue pre...

24 Tom Schaul, et al. ∙

∙ 02/26/2019

The Termination Critic

In this work, we consider the problem of autonomously discovering behavi...

10 Anna Harutyunyan, et al. ∙

∙ 01/30/2019

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement

The ability to transfer skills across tasks has the potential to scale u...

12 Andre Barreto, et al. ∙

∙ 12/18/2018

Universal Successor Features Approximators

The ability of a reinforcement learning (RL) agent to learn about many r...

6 Diana Borsa, et al. ∙

∙ 06/20/2017

Observational Learning by Reinforcement Learning

Observational learning is a type of learning that occurs as a function o...

0 Diana Borsa, et al. ∙

∙ 03/07/2016

Learning Shared Representations in Multi-task Reinforcement Learning

We investigate a paradigm in multi-task reinforcement learning (MT-RL) i...

0 Diana Borsa, et al. ∙

∙ 06/09/2015

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

We consider the problem of modelling noisy but highly symmetric shapes t...

0 Diana Borsa, et al. ∙

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