TADPO: Reinforcement Learning Goes Off-road
The authors introduce TADPO, a new policy gradient method extending Proximal Policy Optimization (PPO) to handle long-horizon planning and difficult exploration in off-road autonomous driving. TADPO combines learning from fixed expert demonstrations with on-policy interactions. This system is integrated into an end-to-end architecture that enables high-speed navigation over extreme terrain, achieving strong performance in simulation and successful zero-shot transfer to a full vehicle.