# Enhancing Robotic Motion Planning in Deep Reinforcement Learning via Task Decomposition

- Source: arXiv cs.RO
- Published: 2026-10-27T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/g1d2x2nfuby3e0hz5mxpwicbb
- Original: https://arxiv.org/abs/2610.35494

## Summary

This study proposes a task decomposition framework for deep reinforcement learning (DRL) that combines transfer learning (TL) with reward redesign (RR) to improve learning efficiency and robustness in obstacle environments.