Researchers have proposed a method for allowing reinforcement learning algorithms to accumulate knowledge while erring on the side of caution. The team, which hails from the University of Toronto, the Vector Institute, and the University of California, Berkeley,  claims this approach can achieve competitive performance while incurring lower catastrophic failure rates during training compared to […]