172 citations · 269 across the 12 of their papers we have counts for
16 papers
Losses, Dissonances, and Distortions
Pablo Samuel Castro
In this paper I present a study in using the losses and gradients obtained during the training of a simple function approximator as a mechanism for creating musical dissonance and…
The Difficulty of Passive Learning in Deep Reinforcement Learning
Georg Ostrovski, Pablo Samuel Castro, Will Dabney
Learning to act from observational data without active environmental interaction is a well-known challenge in Reinforcement Learning (RL). Recent approaches involve constraints on…
Metrics and continuity in reinforcement learning
Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro
In most practical applications of reinforcement learning, it is untenable to maintain direct estimates for individual states; in continuous-state systems, it is impossible. Instead…
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro +1
Reinforcement learning methods trained on few environments rarely learn policies that generalize to unseen environments. To improve generalization, we incorporate the inherent sequ…
GANterpretations
Pablo Samuel Castro
Since the introduction of Generative Adversarial Networks (GANs) [Goodfellow et al., 2014] there has been a regular stream of both technical advances (e.g., Arjovsky et al. [2017])…
Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research
Johan S. Obando-Ceron, Pablo Samuel Castro
Since the introduction of DQN, a vast majority of reinforcement learning research has focused on reinforcement learning with deep neural networks as function approximators. New met…