65 citations · 364 across the 38 of their papers we have counts for
7 papers · 1 filter
Meta Automatic Curriculum Learning
Rémy Portelas, Clément Romac, Katja Hofmann +1
A major challenge in the Deep RL (DRL) community is to train agents able to generalize their control policy over situations never seen in training. Training on diverse tasks has be…
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
Luisa Zintgraf, Leo Feng, Cong Lu +4
To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep. One way to learn such behaviour…
"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games
Mikhail Jacob, Sam Devlin, Katja Hofmann
Game agents such as opponents, non-player characters, and teammates are central to player experiences in many modern games. As the landscape of AI techniques used in the games indu…
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
Rika Antonova, Maksim Maydanskiy, Danica Kragic +2
We address the problem of learning reusable state representations from streaming high-dimensional observations. This is important for areas like Reinforcement Learning (RL), which…
Guaranteeing Reproducibility in Deep Learning Competitions
Brandon Houghton, Stephanie Milani, Nicholay Topin +5
To encourage the development of methods with reproducible and robust training behavior, we propose a challenge paradigm where competitors are evaluated directly on the performance…
Trying AGAIN instead of Trying Longer: Prior Learning for Automatic Curriculum Learning
Rémy Portelas, Katja Hofmann, Pierre-Yves Oudeyer
A major challenge in the Deep RL (DRL) community is to train agents able to generalize over unseen situations, which is often approached by training them on a diversity of tasks (o…
SpatialSim: Recognizing Spatial Configurations of Objects with Graph Neural Networks
Laetitia Teodorescu, Katja Hofmann, Pierre-Yves Oudeyer
Recognizing precise geometrical configurations of groups of objects is a key capability of human spatial cognition, yet little studied in the deep learning literature so far. In pa…