114 citations · 133 across the 10 of their papers we have counts for
7 papers · 1 filter
torchtune: PyTorch native post-training library
Mark Obozov, Maxime Griot, Joseph Cummings +8
Modern LLMs typically require multistage training pipelines to achieve strong downstream performance, with post-training serving as the main interface for adapting open-weight mode…
Learning Controllable Content Generators
Sam Earle, Maria Edwards, Ahmed Khalifa +2
It has recently been shown that reinforcement learning can be used to train generators capable of producing high-quality game levels, with quality defined in terms of some user-spe…
Rotation, Translation, and Cropping for Zero-Shot Generalization
Chang Ye, Ahmed Khalifa, Philip Bontrager +1
Deep Reinforcement Learning (DRL) has shown impressive performance on domains with visual inputs, in particular various games. However, the agent is usually trained on a fixed envi…
PCGRL: Procedural Content Generation via Reinforcement Learning
Ahmed Khalifa, Philip Bontrager, Sam Earle +1
We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design…
Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games
Philip Bontrager, Ahmed Khalifa, Damien Anderson +3
Deep reinforcement learning has learned to play many games well, but failed on others. To better characterize the modes and reasons of failure of deep reinforcement learners, we te…
Deep Reinforcement Learning for General Video Game AI
Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius +2
The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-spec…
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager +3
Deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when neural networks are tr…