Off Environment Evaluation Using Convex Risk Minimization
Pulkit Katdare,Shuijing Liu,Katherine Driggs Campbell,Pulkit Katdare,Shuijing Liu,Katherine Driggs Campbell
Applying reinforcement learning (RL) methods on robots typically involves training a policy in simulation and deploying it on a robot in the real world. Because of the model mismatch between the real world and the simulator, RL agents deployed in this manner tend to perform suboptimally. To tackle this problem, researchers have developed robust policy learning algorithms that rely on synthetic noi...
