172 citations · 530 across the 32 of their papers we have counts for
8 papers · 1 filter
Scalable methods for computing state similarity in deterministic Markov Decision Processes
Pablo Samuel Castro
We present new algorithms for computing and approximating bisimulation metrics in Markov Decision Processes (MDPs). Bisimulation metrics are an elegant formalism that capture behav…
Rigging the Lottery: Making All Tickets Winners
Utku Evci, Trevor Gale, Jacob Menick +2
Many applications require sparse neural networks due to space or inference time restrictions. There is a large body of work on training dense networks to yield sparse networks for…
Inverse Reinforcement Learning with Multiple Ranked Experts
Pablo Samuel Castro, Shijian Li, Daqing Zhang
We consider the problem of learning to behave optimally in a Markov Decision Process when a reward function is not specified, but instead we have access to a set of demonstrators o…
Performing Structured Improvisations with pre-trained Deep Learning Models
Pablo Samuel Castro
The quality of outputs produced by deep generative models for music have seen a dramatic improvement in the last few years. However, most deep learning models perform in "offline"…
A Comparative Analysis of Expected and Distributional Reinforcement Learning
Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare
Since their introduction a year ago, distributional approaches to reinforcement learning (distributional RL) have produced strong results relative to the standard approach which mo…
Distributional reinforcement learning with linear function approximation
Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro +1
Despite many algorithmic advances, our theoretical understanding of practical distributional reinforcement learning methods remains limited. One exception is Rowland et al. (2018)'…
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue
Kory W. Mathewson, Pablo Samuel Castro, Colin Cherry +2
We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives. In this task, the go…
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc G. Bellemare, Will Dabney, Robert Dadashi +6
We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functions. We leverage this perspective to pro…