Critic as Lyapunov function (CALF): a model-free, stability-ensuring agent
<|reference_start|>Critic as Lyapunov function (CALF): a model-free, stability-ensuring agent: This work presents and showcases a novel reinforcement learning agent called Critic As Lyapunov Function (CALF) which is model-free and ensures online environment, in other words, dynamical system stabilization. Online means that in each learning episode, the said environment is stabilized. This, as demonstrated in a case study with a mobile robot simulator, greatly improves the overall learning performance. The base actor-critic scheme of CALF is analogous to SARSA. The latter did not show any success in reaching the target in our studies. However, a modified version thereof, called SARSA-m here, did succeed in some learning scenarios. Still, CALF greatly outperformed the said approach. CALF was also demonstrated to improve a nominal stabilizer provided to it. In summary, the presented agent may be considered a viable approach to fusing classical control with reinforcement learning. Its concurrent approaches are mostly either offline or model-based, like, for instance, those that fuse model-predictive control into the agent.<|reference_end|>
@article{osinenko2024critic,
    title={Critic as Lyapunov function (CALF): a model-free, stability-ensuring
  agent},
    author={Pavel Osinenko, Grigory Yaremenko, Roman Zashchitin, Anton Bolychev,
  Sinan Ibrahim, Dmitrii Dobriborsci},
    journal={arXiv preprint arXiv:2409.09869},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09869},
    primaryClass={cs.RO cs.AI math.OC}
}