34 citations · 52 across the 7 of their papers we have counts for
7 papers
No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
Tiancheng Jin, Junyan Liu, Chloé Rouyer +3
Existing online learning algorithms for adversarial Markov Decision Processes achieve regret after rounds of interactions even if the loss functions are chosen…
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms
Tiancheng Jin, Junyan Liu, Haipeng Luo
We study the problem of designing adaptive multi-armed bandit algorithms that perform optimally in both the stochastic setting and the adversarial setting simultaneously (often kno…
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
Tiancheng Jin, Tal Lancewicki, Haipeng Luo +2
The standard assumption in reinforcement learning (RL) is that agents observe feedback for their actions immediately. However, in practice feedback is often observed in delay. This…
The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition
Tiancheng Jin, Longbo Huang, Haipeng Luo
We consider the best-of-both-worlds problem for learning an episodic Markov Decision Process through episodes, with the goal of achieving re…
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
Tiancheng Jin, Haipeng Luo
This work studies the problem of learning episodic Markov Decision Processes with known transition and bandit feedback. We develop the first algorithm with a ``best-of-both-worlds'…
Learning Adversarial MDPs with Bandit Feedback and Unknown Transition
Chi Jin, Tiancheng Jin, Haipeng Luo +2
We consider the problem of learning in episodic finite-horizon Markov decision processes with an unknown transition function, bandit feedback, and adversarial losses. We propose an…
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
John Holler, Risto Vuorio, Zhiwei Qin +6
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform…