A Safety Aware Model Based Reinforcement Learning Framework for Systems with Uncertainties

17 Nov 2020  ·  Mahmud S M Nahid, Hareland Katrine, Nivison Scott A, Bell Zachary I., Kamalapurkar Rushikesh ·

Safety awareness is critical in reinforcement learning when task restarts are not available and/or when the system is safety critical. Safety requirements are often expressed in terms of state and/or control constraints... In the past, model-based reinforcement learning approaches combined with barrier transformations have been used as an effective tool to learn the optimal control policy under state constraints for systems with fully known models. In this paper, a reinforcement learning technique is developed that utilizes a novel filtered concurrent learning method to realize simultaneous learning and control in the presence of model uncertainties for safety critical systems. read more

PDF Abstract
No code implementations yet. Submit your code now



  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here