Safety Guided Policy Optimization
Dohyeong Kim,Yunho Kim,Kyungjae Lee,Songhwai Oh,Dohyeong Kim,Yunho Kim,Kyungjae Lee,Songhwai Oh
In reinforcement learning (RL), exploration is essential to achieve a globally optimal policy but unconstrained exploration can cause damages to robots and nearby people. To handle this safety issue in exploration, safe RL has been proposed to keep the agent under the specified safety constraints while maximizing cumulative rewards. This paper introduces a new safe RL method which can be applied t...
