← Back to Benchmarks
simmediumrlmetric · varies
Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective
Description
Given a set of trajectories demonstrating the execution of a task safely in a constrained MDP with observable rewards but with unknown constraints and non-observable costs, we aim to find a policy that maximizes the likelihood of demonstrated trajectories trading the balance between being conservative and increasing significantly the likelihood of high-rewarding trajectories but with potentially unsafe steps. Having these objectives, we aim towards learning a policy that maximizes the probabilit