A Multiplicative Value Function for Safe and Efficient Reinforcement Learning
Nick Bührer,Zhejun Zhang,Alexander Liniger,Fisher Yu,Luc Van Gool,Nick Bührer,Zhejun Zhang,Alexander Liniger,Fisher Yu,Luc Van Gool
An emerging field of sequential decision problems is safe Reinforcement Learning (RL), where the objective is to maximize the reward while obeying safety constraints. Being able to handle constraints is essential for deploying RL agents in real-world environments, where constraint violations can harm the agent and the environment. To this end, we propose a safe model-free RL algorithm with a novel...