9 citations · 20 across the 10 of their papers we have counts for
10 papers
Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes
Tim Merino, Sam Earle, Ryunosuke Iwai +2
We introduce Dream-Cubed, a large-scale dataset of Minecraft worlds at voxel resolution, and a family of models using cubes as powerful compositional units for efficient generation…
Finer Behavioral Foundation Models via Auto-Regressive Features and Advantage Weighting
Edoardo Cetin, Ahmed Touati, Yann Ollivier
The forward-backward representation (FB) is a recently proposed framework (Touati et al., 2023; Touati & Ollivier, 2021) to train behavior foundation models (BFMs) that aim at prov…
Simple Ingredients for Offline Reinforcement Learning
Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta +3
Offline reinforcement learning algorithms have proven effective on datasets highly connected to the target downstream task. Yet, leveraging a novel testbed (MOOD) in which trajecto…
Policy Gradient With Serial Markov Chain Reasoning
Edoardo Cetin, Oya Celiktutan
We introduce a new framework that performs decision-making in reinforcement learning (RL) as an iterative reasoning process. We model agent behavior as the steady-state distributio…
Hyperbolic Deep Reinforcement Learning
Edoardo Cetin, Benjamin Chamberlain, Michael Bronstein +1
We propose a new class of deep reinforcement learning (RL) algorithms that model latent representations in hyperbolic space. Sequential decision-making requires reasoning about the…
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels
Edoardo Cetin, Philip J. Ball, Steve Roberts +1
Off-policy reinforcement learning (RL) from pixel observations is notoriously unstable. As a result, many successful algorithms must combine different domain-specific practices and…
Learning Pessimism for Robust and Efficient Off-Policy Reinforcement Learning
Edoardo Cetin, Oya Celiktutan
Off-policy deep reinforcement learning algorithms commonly compensate for overestimation bias during temporal-difference learning by utilizing pessimistic estimates of the expected…
Learning Routines for Effective Off-Policy Reinforcement Learning
Edoardo Cetin, Oya Celiktutan
The performance of reinforcement learning depends upon designing an appropriate action space, where the effect of each action is measurable, yet, granular enough to permit flexible…
IB-DRR: Incremental Learning with Information-Back Discrete Representation Replay
Jian Jiang, Edoardo Cetin, Oya Celiktutan
Incremental learning aims to enable machine learning models to continuously acquire new knowledge given new classes, while maintaining the knowledge already learned for old classes…
Domain-Robust Visual Imitation Learning with Mutual Information Constraints
Edoardo Cetin, Oya Celiktutan
Human beings are able to understand objectives and learn by simply observing others perform a task. Imitation learning methods aim to replicate such capabilities, however, they gen…