94 citations · 292 across the 18 of their papers we have counts for
15 papers · 1 filter
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
Gilad Landau, Miran Özdogan, Gereon Elvers +15
The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact. Among its most promising applications is the restoration of communicat…
In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning
Mikhail Terekhov, Caglar Gulcehre
Multi-objective reinforcement learning (MORL) is essential for addressing the intricacies of real-world RL problems, which often require trade-offs between multiple utility functio…
PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer
Chang Chen, Junyeob Baek, Fei Deng +3
Despite the recent advancements in offline RL, no unified algorithm could achieve superior performance across a broad range of tasks. Offline \textit{value function learning}, in p…
Simple Hierarchical Planning with Diffusion
Chang Chen, Fei Deng, Kenji Kawaguchi +2
Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. However, they often face computational challenges and can falter in general…
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan +21
StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires…
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
Antonio Orvieto, Soham De, Caglar Gulcehre +2
Deep neural networks based on linear RNNs interleaved with position-wise MLPs are gaining traction as competitive approaches for sequence modeling. Examples of such architectures i…
Resurrecting Recurrent Neural Networks for Long Sequences
Antonio Orvieto, Samuel L Smith, Albert Gu +4
Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are hard to optimize and slow to train. Deep state-space models (SSMs) have recently been shown to perfo…
Active Offline Policy Selection
Ksenia Konyushkova, Yutian Chen, Tom Le Paine +5
This paper addresses the problem of policy selection in domains with abundant logged data, but with a restricted interaction budget. Solving this problem would enable safe evaluati…
On Instrumental Variable Regression for Deep Offline Policy Evaluation
Yutian Chen, Liyuan Xu, Caglar Gulcehre +4
We show that the popular reinforcement learning (RL) strategy of estimating the state-action value (Q-function) by minimizing the mean squared Bellman error leads to a regression p…
Regularized Behavior Value Estimation
Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang +7
Offline reinforcement learning restricts the learning process to rely only on logged-data without access to an environment. While this enables real-world applications, it also pose…
Offline Learning from Demonstrations and Unlabeled Experience
Konrad Zolna, Alexander Novikov, Ksenia Konyushkova +6
Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations. Howev…
Hyperparameter Selection for Offline Reinforcement Learning
Tom Le Paine, Cosmin Paduraru, Andrea Michi +5
Offline reinforcement learning (RL purely from logged data) is an important avenue for deploying RL techniques in real-world scenarios. However, existing hyperparameter selection m…
Critic Regularized Regression
Ziyu Wang, Alexander Novikov, Konrad Zolna +8
Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction. It…
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov +15
Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to lea…
Acme: A Research Framework for Distributed Reinforcement Learning
Matthew W. Hoffman, Bobak Shahriari, John Aslanides +36
Deep reinforcement learning (RL) has led to many recent and groundbreaking advances. However, these advances have often come at the cost of both increased scale in the underlying a…