Non-blocking Asynchronous Training for Reinforcement Learning in Real-World Environments
Peter Böhm,Pauline Pounds,Archie C. Chapman,Peter Böhm,Pauline Pounds,Archie C. Chapman
Deep Reinforcement Learning (DRL) faces challenges bridging the sim-to-real gap to enable real-world applications. In contrast to the simulated environments used in conventional DRL training, real-world systems are non-linear and evolve in an asynchronous fashion; sensors and actuators have limited precision; communication channels are noisy; and many components introduce variable delays. While th...
