3 papers
cs.LG2024
SplAgger: Split Aggregation for Meta-Reinforcement Learning
Jacob Beck, Matthew Jackson, Risto Vuorio +2
A core ambition of reinforcement learning (RL) is the creation of agents capable of rapid learning in novel tasks. Meta-RL aims to achieve this by directly learning such agents. Bl…
cs.LG2024
Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control
Zheng Xiong, Risto Vuorio, Jacob Beck +3
Learning a universal policy across different robot morphologies can significantly improve learning efficiency and enable zero-shot generalization to unseen morphologies. However, l…
cs.AI2023
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer +13
A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL). However, constructing a standalone RL policy that maps perception to action directly e…