The authors collect random exploration trajectories and train a conditional energy-based model (CEBM) to estimate temporal log-density ratios for observation pairs, using noise-contrastive estimation.
Random exploration yields planning without reward or policy improvement training
An energy-based model trained on observation pairs via noise-contrastive estimation learns multiscale temporal log-density ratios that guide a closed-loop planner toward goals.
Big Tech
Deqian Kong · Guangyan Sun · Sheng Cheng · Sirui Xie · Bo Pang · Jianwen Xie · +3 more
UCLA · University of Minnesota · Amazon AGI · Salesforce Research · Lambda
Research Digest··2 min read
Kong et al.
Why this paper
From Amazon AGI and 5 others · Part of World Model Planning for Agents, now 25 papers
In one line
Random exploration data enables long-range planning by learning temporal log-density ratios at multiple horizons without policy improvement training.
What we could check
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- ·No stated limitations found
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