RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model

no code implementations • 18 Mar 2024 • Junyi Fan, Yuxuan Han, Jialin Zeng, Jian-Feng Cai, Yang Wang, Yang Xiang, Jiheng Zhang

Up to a logarithmic dependence on the size of the state space, Lin-Confident-FTRL learns $\epsilon$-CCE with a provable optimal accuracy bound $O(\epsilon^{-2})$ and gets rids of the linear dependency on the action space, while scaling polynomially with relevant problem parameters (such as the number of agents and time horizon).

Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles

1 code implementation • 21 Oct 2022 • Yuxuan Han, Jialin Zeng, Yang Wang, Yang Xiang, Jiheng Zhang

We study the stochastic contextual bandit with knapsacks (CBwK) problem, where each action, taken upon a context, not only leads to a random reward but also costs a random resource consumption in a vector form.

Multi-Armed Bandits regression