Adapting to the “Open World”: The Utility of Hybrid Hierarchical Reinforcement Learning and Symbolic Planning
Pierrick Lorang,Helmut Horvath,Tobias Kietreiber,Patrik Zips,Clemens Heitzinger,Matthias Scheutz,Pierrick Lorang,Helmut Horvath,Tobias Kietreiber,Patrik Zips,Clemens Heitzinger,Matthias Scheutz
Open-world robotic tasks such as autonomous driving pose significant challenges to robot control due to unknown and unpredictable events that disrupt task performance. Neural network-based reinforcement learning (RL) techniques (like DQN, PPO, SAC, etc.) struggle to adapt in large domains and suffer from catastrophic forgetting. Hybrid planning and RL approaches have shown some promise in handling...
