mlsanityData to 14 Oct 2026

Hysteresis-Based RL: Robustifying Reinforcement Learning-based Control Policies via Hybrid Control

arXiv 2204.00654 ↗First posted 1 Apr 2022cs.LG cs.AI cs.SYcited by 0

Not on the map. Why?

Abstract (arXiv)

Reinforcement learning (RL) is a promising approach for deriving control policies for complex systems. As we show in two control problems, the derived policies from using the Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) algorithms may lack robustness guarantees. Motivated by these issues, we propose a new hybrid algorithm, which we call Hysteresis-Based RL (HyRL), augmenting an existing RL algorithm with hysteresis switching and two stages of learning. We illustrate its properties in two examples for which PPO and DQN fail.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 1 Apr 2022.

Builds on

8 of 17 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  5. 2019Action Robust Reinforcement Learning and Applications in Continuous Controlcited by 174
  6. 2021Robust Reinforcement Learning on State Observations with Learned Optimal Adversarycited by 135
  7. 2019Robust Reinforcement Learning for Continuous Control with Model Misspecificationcited by 78
  8. 2019Certified Adversarial Robustness for Deep Reinforcement Learningcited by 47

Built on by

Not cited yet.