# ε-Policy Gradient for Online Pricing

arXiv: https://arxiv.org/abs/2405.03624 · HTML: https://mlsanity.com/paper/2405.03624/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 6 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 14 of 14)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Condition](https://mlsanity.com/paper/1608.04636/) (2016)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Loss landscapes and optimization in over-parameterized non-linear systems and neural networks](https://mlsanity.com/paper/2003.00307/) (2020)
- [On Information Gain and Regret Bounds in Gaussian Process Bandits](https://mlsanity.com/paper/2009.06966/) (2020)
- [On the Convergence of SGD with Biased Gradients](https://mlsanity.com/paper/2008.00051/) (2020)
- [Adaptive Exploration in Linear Contextual Bandit](https://mlsanity.com/paper/1910.06996/) (2019)
- [Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon](https://mlsanity.com/paper/2006.15316/) (2020)
- [Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls](https://mlsanity.com/paper/2104.09311/) (2021)
- [Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models](https://mlsanity.com/paper/2112.10264/) (2021)
- [Exploration via linearly perturbed loss minimisation](https://mlsanity.com/paper/2311.07565/) (2023)
- [Logarithmic regret bounds for continuous-time average-reward Markov decision processes](https://mlsanity.com/paper/2205.11168/) (2022)
- [Square-root regret bounds for continuous-time episodic Markov decision processes](https://mlsanity.com/paper/2210.00832/) (2022)
- [Generalised correlated batched bandits via the ARC algorithm with application to dynamic pricing](https://mlsanity.com/paper/2102.04263/) (2021)

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective](https://mlsanity.com/paper/2406.12983/) (2024)

## Abstract (arXiv)

Combining model-based and model-free reinforcement learning approaches, this paper proposes and analyzes an ε-policy gradient algorithm for the online pricing learning task. The algorithm extends ε-greedy algorithm by replacing greedy exploitation with gradient descent step and facilitates learning via model inference. We optimize the regret of the proposed algorithm by quantifying the exploration cost in terms of the exploration probability ε and the exploitation cost in terms of the gradient descent optimization and gradient estimation errors. The algorithm achieves an expected regret of order O(√T) (up to a logarithmic factor) over T trials.