10 papers
Provably Efficient Reinforcement Learning in Continuous-Time Episodic MDPs with Poisson Decision Epochs
Kenny Guo, Valentio Iverson, Sahan Wijetunga +1
Many real-world reinforcement learning (RL) problems evolve in continuous time, where decisions occur at irregular, event-driven intervals rather than at fixed discrete steps. We s…
The Price of Decentralization in Top- Arm Identification
Larissa Xu, Jasmine Nguyen, William Chang
Cooperative teams often need to agree on the best few options rather than simply accumulate reward, and they must do so while each member sees only a fragment of the team's collect…
Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry
Larissa Xu, King Bi, William Chang
We study decentralized multi-player reinforcement learning in episodic tabular Markov decision processes (MDPs) under three forms of information asymmetry: (A) unobserved actions w…
DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks
Andy Wang, Charlton Shih, William Chang
In this work, we extend the Dependent Click Model (DCM) Bandits to a multiplayer information-asymmetric setting, where multiple agents interact with a shared ranked list and may ob…
Coordinating the Unknown Lipschitz Constant in Multiplayer Bandits
Ricardo Parada, Chenzhang Zhao, William Chang
Motivated by decentralized applications, we study cooperative multi-agent bandits in continuous (Lipschitz) action spaces when the Lipschitz constant is unknown. We consider three…
Multiplayer Information Asymmetric Contextual Bandits
William Chang, Yuanhao Lu
Single-player contextual bandits are a well-studied problem in reinforcement learning that has seen applications in various fields such as advertising, healthcare, and finance. In…
Multiplayer Information Asymmetric Bandits in Metric Spaces
William Chang, Aditi Karthik
In recent years the information asymmetric Lipschitz bandits In this paper we studied the Lipschitz bandit problem applied to the multiplayer information asymmetric problem studied…
Mixing on Generalized Associahedra
William Chang, Colin Defant, Daniel Frishberg
Eppstein and Frishberg recently proved that the mixing time for the simple random walk on the -skeleton of the associahedron is . We obtain similar rapid mixing…
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs
Tianyuan Jin, Hao-Lun Hsu, William Chang +1
We study the multi-agent multi-armed bandit (MAMAB) problem, where agents are factored into overlapping groups. Each group represents a hyperedge, forming a hypergraph over…
Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communication
William Chang, Yuanhao Lu
We consider a cooperative multiplayer bandit learning problem where the players are only allowed to agree on a strategy beforehand, but cannot communicate during the learning proce…