22 citations · 33 across the 3 of their papers we have counts for
3 papers
RLAX: Large-Scale, Distributed Reinforcement Learning for Large Language Models on TPUs
Runlong Zhou, Lefan Zhang, Shang-Chen Wu +29
Reinforcement learning (RL) has emerged as the de-facto paradigm for improving the reasoning capabilities of large language models (LLMs). We have developed RLAX, a scalable RL fra…
Intelligent Roundabout Insertion using Deep Reinforcement Learning
Alessandro Paolo Capasso, Giulio Bacchiani, Daniele Molinari
An important topic in the autonomous driving research is the development of maneuver planning systems. Vehicles have to interact and negotiate with each other so that optimal choic…
Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning
Giulio Bacchiani, Daniele Molinari, Marco Patander
Expert human drivers perform actions relying on traffic laws and their previous experience. While traffic laws are easily embedded into an artificial brain, modeling human complex…