UAS Navigation in the Real World Using Visual Observation

no code implementations • 25 Aug 2022 • Yuci Han, Jianli Wei, Alper Yilmaz

Once the agent learns the navigation policy, which means 'familiarized themselves with the environment', we let the UAS fly in the real world to recognize the landmarks using image matching method and take action according to the learned policy.

Landmark Recognition Reinforcement Learning (RL) +1

Learning to Drive Using Sparse Imitation Reinforcement Learning

no code implementations • 24 May 2022 • Yuci Han, Alper Yilmaz

In this paper, we propose Sparse Imitation Reinforcement Learning (SIRL), a hybrid end-to-end control policy that combines the sparse expert driving knowledge with reinforcement learning (RL) policy for autonomous driving (AD) task in CARLA simulation environment.

Autonomous Driving reinforcement-learning +2