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

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