# On the Complexity of Adversarial Decision Making

@article{Foster2022OnTC, title={On the Complexity of Adversarial Decision Making}, author={Dylan J. Foster and Alexander Rakhlin and Ayush Sekhari and Karthik Sridharan}, journal={ArXiv}, year={2022}, volume={abs/2206.13063} }

A central problem in online learning and decision making—from bandits to reinforcement learning—is to understand what modeling assumptions lead to sample-eﬃcient learning guarantees. We consider a general adversarial decision making framework that encompasses (structured) bandit problems with adversarial rewards and reinforcement learning problems with adversarial dynamics. Our main result is to show—via new upper and lower bounds—that the Decision-Estimation Coeﬃcient, a complexity measure…

## One Citation

### Unified Algorithms for RL with Decision-Estimation Coefficients: No-Regret, PAC, and Reward-Free Learning

- Computer Science
- 2022

Two new DEC-type complexity measures are proposed: Explorative DEC (EDEC), and Reward-Free DEC (RFDEC) which are shown to be necessary and sufficient for sample-efficient PAC learning and reward-free learning, thereby extending the original DEC which only captures no-regret learning.

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