Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots
Yufeng Yuan,A. Rupam Mahmood,Yufeng Yuan,A. Rupam Mahmood
An oft-ignored challenge of real-world reinforcement learning is that the real world does not pause when agents make learning updates. As standard simulated environments do not address this real-time aspect of learning, most available implementations of RL algorithms process environment interactions and learning updates sequentially. As a consequence, when such implementations are deployed in the ...
