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Distributed No-Regret Learning for Multi-Stage Systems with End-to-End Bandit Feedback
Machine Learning · Submitted 6 Apr 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We theoretically prove that the $\epsilon-$EXP3 algorithm is a no-regret policy that achieves sublinear regret.
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The abstract, sentence by sentence
This paper studies multi-stage systems with end-to-end bandit feedback. In such systems, each job needs to go through multiple stages, each managed by a different agent, before generating an outcome. Each agent can only control its own action and learn the final outcome of the job. It has neither knowledge nor control on actions taken by agents in the next stage. The goal of this paper is to develop distributed online learning algorithms that achieve sublinear regret in adversarial environments. The setting of this paper significantly expands the traditional multi-armed bandit problem, which considers only one agent and one stage. In addition to the exploration-exploitation dilemma in the traditional multi-armed bandit problem, we show that the consideration of multiple stages introduces a third component, education, where an agent needs to choose its actions to facilitate the learning of agents in the next stage. To solve this newly introduced exploration-exploitation-education trilemma, we propose a simple distributed online learning algorithm, $\epsilon-$EXP3. We theoretically prove that the $\epsilon-$EXP3 algorithm is a no-regret policy that achieves sublinear regret. Simulation results show that the $\epsilon-$EXP3 algorithm significantly outperforms existing no-regret online learning algorithms for the traditional multi-armed bandit problem.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | This paper studies multi-stage systems with end-to-end bandit feedback. | background 0.69 | 0.26 | 0.03 | asserted 0.92 |
| s1 | In such systems, each job needs to go through multiple stages, each managed by a different agent, before generating an outcome. | background 1.00 | 0.11 | 0.02 | asserted 1.00 |
| s2 | Each agent can only control its own action and learn the final outcome of the job. | background 1.00 | 0.10 | 0.02 | asserted 1.00 |
| s3 | It has neither knowledge nor control on actions taken by agents in the next stage. | background 0.99 | 0.11 | 0.02 | asserted 1.00 |
| s4 | The goal of this paper is to develop distributed online learning algorithms that achieve sublinear regret in adversarial environments. | contribution 0.92 | 0.32 | 0.11 | asserted 0.79 |
| s5 | The setting of this paper significantly expands the traditional multi-armed bandit problem, which considers only one agent and one stage. | background 0.80 | 0.19 | 0.03 | asserted 1.00 |
| s6 | In addition to the exploration-exploitation dilemma in the traditional multi-armed bandit problem, we show that the consideration of multiple stages introduces a third component, education, where an agent needs to choose its actions to facilitate the learning of agents in the next stage. | contribution 0.57 | 0.88 | 0.13 | asserted 1.00 |
| s7 | To solve this newly introduced exploration-exploitation-education trilemma, we propose a simple distributed online learning algorithm, $\epsilon-$EXP3. | method 0.61 | 0.95 | 0.17 | asserted 1.00 |
| s8 | We theoretically prove that the $\epsilon-$EXP3 algorithm is a no-regret policy that achieves sublinear regret. | result 0.70 | 0.96 | 0.61 | asserted 1.00 |
| s9 | Simulation results show that the $\epsilon-$EXP3 algorithm significantly outperforms existing no-regret online learning algorithms for the traditional multi-armed bandit problem. | result 1.00 | 0.94 | 0.24 | asserted 1.00 |
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Original abstract
This paper studies multi-stage systems with end-to-end bandit feedback. In such systems, each job needs to go through multiple stages, each managed by a different agent, before generating an outcome. Each agent can only control its own action and learn the final outcome of the job. It has neither knowledge nor control on actions taken by agents in the next stage. The goal of this paper is to develop distributed online learning algorithms that achieve sublinear regret in adversarial environments. The setting of this paper significantly expands the traditional multi-armed bandit problem, which considers only one agent and one stage. In addition to the exploration-exploitation dilemma in the traditional multi-armed bandit problem, we show that the consideration of multiple stages introduces a third component, education, where an agent needs to choose its actions to facilitate the learning of agents in the next stage. To solve this newly introduced exploration-exploitation-education trilemma, we propose a simple distributed online learning algorithm, $\epsilon-$EXP3. We theoretically prove that the $\epsilon-$EXP3 algorithm is a no-regret policy that achieves sublinear regret. Simulation results show that the $\epsilon-$EXP3 algorithm significantly outperforms existing no-regret online learning algorithms for the traditional multi-armed bandit problem.
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