Pablo Samuel Castro

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Metrics and continuity in reinforcement learning

In most practical applications of reinforcement learning, it is untenabl...
02/02/2021 ∙ by Charline Le Lan, et al. ∙ 10 ∙ share

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Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning

Reinforcement learning methods trained on few environments rarely learn ...
01/13/2021 ∙ by Rishabh Agarwal, et al. ∙ 21 ∙ share

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

Since the introduction of DQN, a vast majority of reinforcement learning...
11/20/2020 ∙ by Johan S. Obando-Ceron, et al. ∙ 0 ∙ share

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GANterpretations

Since the introduction of Generative Adversarial Networks (GANs) [Goodfe...
11/06/2020 ∙ by Pablo Samuel Castro, et al. ∙ 0 ∙ share

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Rigging the Lottery: Making All Tickets Winners

Sparse neural networks have been shown to be more parameter and compute ...
11/25/2019 ∙ by Utku Evci, et al. ∙ 0 ∙ share

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Scalable methods for computing state similarity in deterministic Markov Decision Processes

We present new algorithms for computing and approximating bisimulation m...
11/21/2019 ∙ by Pablo Samuel Castro, et al. ∙ 49 ∙ share

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Inverse Reinforcement Learning with Multiple Ranked Experts

We consider the problem of learning to behave optimally in a Markov Deci...
07/31/2019 ∙ by Pablo Samuel Castro, et al. ∙ 5 ∙ share

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Performing Structured Improvisations with pre-trained Deep Learning Models

The quality of outputs produced by deep generative models for music have...
04/30/2019 ∙ by Pablo Samuel Castro, et al. ∙ 0 ∙ share

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Distributional reinforcement learning with linear function approximation

Despite many algorithmic advances, our theoretical understanding of prac...
02/08/2019 ∙ by Marc G. Bellemare, et al. ∙ 18 ∙ share

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A Geometric Perspective on Optimal Representations for Reinforcement Learning

This paper proposes a new approach to representation learning based on g...
01/31/2019 ∙ by Marc G. Bellemare, et al. ∙ 10 ∙ share

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

We consider the problem of designing an artificial agent capable of inte...
01/31/2019 ∙ by Kory W. Mathewson, et al. ∙ 8 ∙ share

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A Comparative Analysis of Expected and Distributional Reinforcement Learning

Since their introduction a year ago, distributional approaches to reinfo...
01/30/2019 ∙ by Clare Lyle, et al. ∙ 6 ∙ share

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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...
12/17/2018 ∙ by Felipe Petroski Such, et al. ∙ 6 ∙ share

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Dopamine: A Research Framework for Deep Reinforcement Learning

Deep reinforcement learning (deep RL) research has grown significantly i...
12/14/2018 ∙ by Pablo Samuel Castro, et al. ∙ 1 ∙ share

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Combining Learned Lyrical Structures and Vocabulary for Improved Lyric Generation

The use of language models for generating lyrics and poetry has received...
11/12/2018 ∙ by Pablo Samuel Castro, et al. ∙ 0 ∙ share

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