Deep Q-learning: a robust control approach
Preprint, 2022

In this paper, we place deep Q-learning into a control-oriented perspective and study its learning dynamics with well-established techniques from robust control. We formulate an uncertain linear time-invariant model by means of the neural tangent kernel to describe learning. We show the instability of learning and analyze the agent's behavior in frequency-domain. Then, we ensure convergence via robust controllers acting as dynamical rewards in the loss function. We synthesize three controllers: state-feedback gain scheduling 2, dynamic ∞, and constant gain ∞ controllers. Setting up the learning agent with a control-oriented tuning methodology is more transparent and has well-established literature compared to the heuristics in reinforcement learning. In addition, our approach does not use a target network and randomized replay memory. The role of the target network is overtaken by the control input, which also exploits the temporal dependency of samples (opposed to a randomized memory buffer). Numerical simulations in different OpenAI Gym environments suggest that the ∞ controlled learning performs slightly better than Double deep Q-learning.


Balázs Varga

Chalmers, Elektroteknik, System- och reglerteknik

Balázs Adam Kulcsár

Chalmers, Elektroteknik, System- och reglerteknik

Morteza Haghir Chehreghani

Chalmers, Data- och informationsteknik, Data Science och AI

Real-Time Robust and AdaptIve Learning in ElecTric VEhicles (RITE)

Chalmers, 2020-01-01 -- 2021-12-31.

Chalmers AI-forskningscentrum (CHAIR), 2020-01-01 -- 2021-12-31.




Data- och informationsvetenskap

Transportteknik och logistik

Robotteknik och automation


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