Papers › Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge
Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge
Meshal Alharbi, Mardavij Roozbehani, Munther Dahleh
The problem of sample complexity of online reinforcement learning is often studied in the literature without taking into account any partial knowledge about the system dynamics that could potentially accelerate the learning process. In this paper, we study the sample complexity of online Q-learning methods when some prior knowledge about the dynamics is available or can be learned efficiently. We focus on systems that evolve according to an additive disturbance model of the form Sₕ₊₁ = f(Sₕ, Aₕ) + Wₕ, where f represents the underlying system dynamics, and Wₕ are unknown disturbances independent of states and actions. In the setting of finite episodic Markov decision processes with S states, A actions, and episode length H, we present an optimistic Q-learning algorithm that achieves 𝒪̃(Poly(H)√(T)) regret under perfect knowledge of f, where T is the total number of interactions with the system. This is in contrast to the typical 𝒪̃(Poly(H)√(SAT)) regret for existing Q-learning methods. Further, if only a noisy estimate f̂ of f is available, our method can learn an approximately optimal policy in a number of samples that is independent of the cardinalities of state and action spaces. The sub-optimality gap depends on the approximation error f̂-f, as well as the Lipschitz constant of the corresponding optimal value function. Our approach does not require modeling of the transition probabilities and enjoys the same memory complexity as model-free methods.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections