OPTS-TTPO: Enhancing Finite-Sample Policy-Gradient Learning with Tree Search
The policy-gradient theorem gives the exact gradient under the current policy, but finite on-policy samples may miss rare high-return trajectories. We study whether tree search improves their coverage within a fixed budget while controlling gradient bias. We introduce On-Policy Parallel Tree Search (OPTS) and Tree Trajectory Policy Optimization (TTPO) using on-policy tree trajectories, which sample new suffixes from the current policy at visited states. This needs no action-distribution correction, although branching changes state visitation. Our Branch Aggregation Lemma shows that branch-weighted tree statistics recover chain expectations when branch choices and weights are fixed before ...
| Authors | Junyu Lu, Shichao Weng, Zhiqiang Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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