46 citations · 63 across the 16 of their papers we have counts for
3 papers · 1 filter
GenRL: Multimodal-foundation world models for generalization in embodied agents
Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt +2
Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement learning (RL) is hard to scale up as it requir…
Choreographer: Learning and Adapting Skills in Imagination
Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt +2
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with the ability to control and influence the env…
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen +4
Controlling artificial agents from visual sensory data is an arduous task. Reinforcement learning (RL) algorithms can succeed but require large amounts of interactions between the…