172 citations · 530 across the 32 of their papers we have counts for
8 papers · 1 filter
Malakai: Music That Adapts to the Shape of Emotions
Zack Harris, Liam Atticus Clarke, Pietro Gagliano +3
The advent of ML music models such as Google Magenta's MusicVAE now allow us to extract and replicate compositional features from otherwise complex datasets. These models allow com…
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…
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro +2
Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing their relative performance on a large suite of tasks. Most published results on deep RL benchma…
A general class of surrogate functions for stable and efficient reinforcement learning
Sharan Vaswani, Olivier Bachem, Simone Totaro +6
Common policy gradient methods rely on the maximization of a sequence of surrogate functions. In recent years, many such surrogate functions have been proposed, most without strong…
MICo: Improved representations via sampling-based state similarity for Markov decision processes
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden +1
We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an effective means of shaping the learnt repres…
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…