Pablo Samuel Castro

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∙ 06/17/2022

The State of Sparse Training in Deep Reinforcement Learning

The use of sparse neural networks has seen rapid growth in recent years,...

Laura Graesser, et al. ∙

∙ 06/03/2022

Beyond Tabula Rasa: Reincarnating Reinforcement Learning

Learning tabula rasa, that is without any prior knowledge, is the preval...

Rishabh Agarwal, et al. ∙

∙ 11/08/2021

Losses, Dissonances, and Distortions

In this paper I present a study in using the losses and gradients obtain...

Pablo Samuel Castro, et al. ∙

∙ 10/26/2021

The Difficulty of Passive Learning in Deep Reinforcement Learning

Learning to act from observational data without active environmental int...

Georg Ostrovski, et al. ∙

∙ 08/30/2021

Deep Reinforcement Learning at the Edge of the Statistical Precipice

Deep reinforcement learning (RL) algorithms are predominantly evaluated ...

Rishabh Agarwal, et al. ∙

∙ 08/12/2021

A functional mirror ascent view of policy gradient methods with function approximation

We use functional mirror ascent to propose a general framework (referred...

Sharan Vaswani, et al. ∙

∙ 02/02/2021

Metrics and continuity in reinforcement learning

In most practical applications of reinforcement learning, it is untenabl...

Charline Le Lan, et al. ∙

∙ 01/13/2021

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning

Reinforcement learning methods trained on few environments rarely learn ...

Rishabh Agarwal, et al. ∙

∙ 11/20/2020

Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research

Since the introduction of DQN, a vast majority of reinforcement learning...

Johan S. Obando-Ceron, et al. ∙

∙ 11/06/2020

GANterpretations

Since the introduction of Generative Adversarial Networks (GANs) [Goodfe...

Pablo Samuel Castro, et al. ∙

∙ 11/25/2019

Rigging the Lottery: Making All Tickets Winners

Sparse neural networks have been shown to be more parameter and compute ...

Utku Evci, et al. ∙

∙ 11/21/2019

Scalable methods for computing state similarity in deterministic Markov Decision Processes

We present new algorithms for computing and approximating bisimulation m...

Pablo Samuel Castro, et al. ∙

∙ 07/31/2019

Inverse Reinforcement Learning with Multiple Ranked Experts

We consider the problem of learning to behave optimally in a Markov Deci...

Pablo Samuel Castro, et al. ∙

∙ 04/30/2019

Performing Structured Improvisations with pre-trained Deep Learning Models

The quality of outputs produced by deep generative models for music have...

Pablo Samuel Castro, et al. ∙

∙ 02/08/2019

Distributional reinforcement learning with linear function approximation

Despite many algorithmic advances, our theoretical understanding of prac...

Marc G. Bellemare, et al. ∙

∙ 01/31/2019

A Geometric Perspective on Optimal Representations for Reinforcement Learning

This paper proposes a new approach to representation learning based on g...

Marc G. Bellemare, et al. ∙

∙ 01/31/2019

Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue

We consider the problem of designing an artificial agent capable of inte...

Kory W. Mathewson, et al. ∙

∙ 01/30/2019

A Comparative Analysis of Expected and Distributional Reinforcement Learning

Since their introduction a year ago, distributional approaches to reinfo...

Clare Lyle, et al. ∙

∙ 12/17/2018

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

Much human and computational effort has aimed to improve how deep reinfo...

Felipe Petroski Such, et al. ∙

∙ 12/14/2018

Dopamine: A Research Framework for Deep Reinforcement Learning

Deep reinforcement learning (deep RL) research has grown significantly i...

Pablo Samuel Castro, et al. ∙

∙ 11/12/2018

Combining Learned Lyrical Structures and Vocabulary for Improved Lyric Generation

The use of language models for generating lyrics and poetry has received...

Pablo Samuel Castro, et al. ∙