Feature Extraction for Effective and Efficient Deep Reinforcement Learning on Real Robotic Platforms
Peter Böhm,Pauline Pounds,Archie C. Chapman,Peter Böhm,Pauline Pounds,Archie C. Chapman
Deep reinforcement learning (DRL) methods can solve complex continuous control tasks in simulated environments by taking actions based solely on state observations at each decision point. Because of the dynamics involved, individual snapshots of real-world sensor measurements afford only partial state observability, so it is typical to use a history of observations to improve training and policy p...
