In our new tiny paper accepted at the ICLR workshop on world models we introduce Dreamer-CDP, a Dreamer variant that learns a world model without reconstructing raw pixel observations. Preprint: https://arxiv.org/abs/2603.07083 Standard model-based reinforcement learningContinue reading
Tag: iclr
Improving equilibrium propagation without weight symmetry through Jacobian homeostasis
We are happy our new paper “Improving equilibrium propagation (EP) without weight symmetry through Jacobian homeostasis,” led by Axel accepted at ICLR 2024. Preprint: https://arxiv.org/abs/2309.02214Code: https://github.com/Laborieux-Axel/generalized-holo-ep EP prescribes a local learning rule and uses recurrentContinue reading