Segmentation Invariance Critical for Offline Goal-Conditioned Reinforcement Learning
Key takeaways
- Arbitrary segmentation of logged trajectories severely degrades offline GCRL performance if handled naively.
- "Continuation-valid targets" (CVT) are essential for maintaining performance across segmented data.
- The SegBench-GC benchmark provides a controlled way to test segmentation invariance.
- Proper data preprocessing for offline RL must account for trajectory continuity.
Who benefits
Summary
This paper introduces SegBench-GC, a benchmark to stress-test the impact of artificial trajectory segmentation on offline goal-conditioned reinforcement learning (GCRL). It demonstrates that administrative cuts in logged trajectories, if treated naively, severely degrade GCRL performance, highlighting the importance of continuation-valid targets.
Why it matters
Professionals developing or deploying offline reinforcement learning systems, especially in robotics or autonomous agents, must be aware of how data segmentation impacts model performance and implement strategies to ensure segmentation invariance.
How to implement this in your domain
- 1Analyze existing offline reinforcement learning datasets for potential arbitrary trajectory segmentations.
- 2Adopt "continuation-valid targets" (CVT) or similar methods to preserve continuation value across trajectory cuts.
- 3Utilize benchmarks like SegBench-GC to stress-test the robustness of GCRL algorithms to different segmentation strategies.
- 4Develop data preprocessing pipelines that explicitly account for and mitigate the negative effects of administrative trajectory segmentation.
Original post by Musa Shams
"arXiv:2608.27678v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) often uses trajectory structure for future-goal sampling and multi-step targets, yet logged trajectories may be partitioned for administrative reasons that do not correspond to…"
View on XOriginally posted by Musa Shams on X · view source
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