Pablo Samuel Castro | DeepAI

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∙ 07/25/2023

Offline Reinforcement Learning with On-Policy Q-Function Regularization

The core challenge of offline reinforcement learning (RL) is dealing wit...

0 Laixi Shi, et al. ∙

∙ 06/24/2023

Minigrid Miniworld: Modular Customizable Reinforcement Learning Environments for Goal-Oriented Tasks

We present the Minigrid and Miniworld libraries which provide a suite of...

0 Maxime Chevalier-Boisvert, et al. ∙

∙ 05/30/2023

Bigger, Better, Faster: Human-level Atari with human-level efficiency

We introduce a value-based RL agent, which we call BBF, that achieves su...

0 Max Schwarzer, et al. ∙

∙ 04/27/2023

JaxPruner: A concise library for sparsity research

This paper introduces JaxPruner, an open-source JAX-based pruning and sp...

4 Joo Hyung Lee, et al. ∙

∙ 04/25/2023

Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks

Auxiliary tasks improve the representations learned by deep reinforcemen...

0 Jesse Farebrother, et al. ∙

∙ 02/24/2023

The Dormant Neuron Phenomenon in Deep Reinforcement Learning

In this work we identify the dormant neuron phenomenon in deep reinforce...

0 Ghada Sokar, et al. ∙

∙ 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,...

0 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...

0 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...

0 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...

0 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 ...

0 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...

13 Sharan Vaswani, et al. ∙

∙ 02/02/2021

Metrics and continuity in reinforcement learning

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

10 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 ...

21 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...

0 Johan S. Obando-Ceron, et al. ∙

∙ 11/06/2020

GANterpretations

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

0 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 ...

0 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...

49 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...

5 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...

0 Pablo Samuel Castro, et al. ∙

∙ 02/08/2019

Distributional reinforcement learning with linear function approximation

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

18 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...

10 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...

8 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...

6 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...

6 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...

1 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...

0 Pablo Samuel Castro, et al. ∙

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