TADPO: Reinforcement Learning Goes Off-road
Off-road autonomous driving poses significant challenges such as navigating unmapped, variable terrain with uncertain and diverse dynamics. Addressing these challenges requires effective long-horizon planning and adaptable control. Reinforcement Learning (RL) offers a promising solution by learning control policies directly from interaction. However, because off-road driving is a long-horizon task with low-signal rewards, standard RL methods are challenging to apply in this setting. We introduce TADPO, a novel policy gradient formulation that extends Proximal Policy Optimization (PPO), leveraging off-policy trajectories for teacher guidance and on-policy trajectories for student explorati...
| Authors | Zhouchonghao Wu, Raymond Song, Vedant Mundheda et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 March 2026 |
| Last updated at source | 13 July 2026 |
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