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cs.LG2025
One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
Yuan Pu, Yazhe Niu, Jia Tang +3
In heterogeneous multi-task decision-making, tasks not only exhibit diverse observation and action spaces but also vary substantially in their underlying complexities. While conven…
cs.LG2024
UniZero: Generalized and Efficient Planning with Scalable Latent World Models
Yuan Pu, Yazhe Niu, Zhenjie Yang +3
Learning predictive world models is crucial for enhancing the planning capabilities of reinforcement learning (RL) agents. Recently, MuZero-style algorithms, leveraging the value e…
cs.LG2023★ 2 cited
LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
Yazhe Niu, Yuan Pu, Zhenjie Yang +6
Building agents based on tree-search planning capabilities with learned models has achieved remarkable success in classic decision-making problems, such as Go and Atari. However, i…