172 citations · 530 across the 32 of their papers we have counts for
7 papers · 1 filter
A Kernel Perspective on Behavioural Metrics for Markov Decision Processes
Pablo Samuel Castro, Tyler Kastner, Prakash Panangaden +1
Behavioural metrics have been shown to be an effective mechanism for constructing representations in reinforcement learning. We present a novel perspective on behavioural metrics f…
Offline Reinforcement Learning with On-Policy Q-Function Regularization
Laixi Shi, Robert Dadashi, Yuejie Chi +2
The core challenge of offline reinforcement learning (RL) is dealing with the (potentially catastrophic) extrapolation error induced by the distribution shift between the history d…
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers +6
We present the Minigrid and Miniworld libraries which provide a suite of goal-oriented 2D and 3D environments. The libraries were explicitly created with a minimalistic design para…
Bigger, Better, Faster: Human-level Atari with human-level efficiency
Max Schwarzer, Johan Obando-Ceron, Aaron Courville +3
We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used for value…
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal +4
Auxiliary tasks improve the representations learned by deep reinforcement learning agents. Analytically, their effect is reasonably well understood; in practice, however, their pri…
JaxPruner: A concise library for sparsity research
Joo Hyung Lee, Wonpyo Park, Nicole Mitchell +22
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research. JaxPruner aims to accelerate research on sparse neural…
The Dormant Neuron Phenomenon in Deep Reinforcement Learning
Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro +1
In this work we identify the dormant neuron phenomenon in deep reinforcement learning, where an agent's network suffers from an increasing number of inactive neurons, thereby affec…