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Leveraging Separated World Model for Exploration in Visually Distracted Environments
Abstract Model-based unsupervised reinforcement learning (URL) has gained prominence
for reducing environment interactions and learning general skills using intrinsic rewards …
for reducing environment interactions and learning general skills using intrinsic rewards …
TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer
Model-based RL is a promising approach for real-world robotics due to its improved sample
efficiency and generalization capabilities compared to model-free RL. However, effective …
efficiency and generalization capabilities compared to model-free RL. However, effective …
Learning Curricula in Open-Ended Worlds
M Jiang - arxiv preprint arxiv:2312.03126, 2023 - arxiv.org
Deep reinforcement learning (RL) provides powerful methods for training optimal sequential
decision-making agents. As collecting real-world interactions can entail additional costs and …
decision-making agents. As collecting real-world interactions can entail additional costs and …
Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G
The introduction of intelligent interconnectivity between the physical and human worlds has
attracted great attention for future sixth-generation (6G) networks, emphasizing massive …
attracted great attention for future sixth-generation (6G) networks, emphasizing massive …
PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies
M Byrd, J Crandell, M Das, J Inman, R Wright… - arxiv preprint arxiv …, 2025 - arxiv.org
Numerous real-world control problems involve dynamics and objectives affected by
unobservable hidden pa-rameters, ranging from autonomous driving to robotic manipu …
unobservable hidden pa-rameters, ranging from autonomous driving to robotic manipu …
Reinforcing automated machine learning-bridging AutoML and reinforcement learning
T Eimer - 2024 - repo.uni-hannover.de
Reinforcement learning is a machine learning paradigm that allows learning through
interaction. It intertwines data collection and model training into a single problem statement …
interaction. It intertwines data collection and model training into a single problem statement …
Investigating Online RL in World Models
Significant advances in online reinforcement learning (RL) remain limited by the need for
extensive environment interaction or accurate simulators. World models trained on large …
extensive environment interaction or accurate simulators. World models trained on large …