Approximation of Convex Envelope Using Reinforcement Learning

Vivek S. Borkar, Adit Akarsh
11/24/2023
eess.SYcs.LG

No confident code implementation found for this paper.

Abstract

Oberman gave a stochastic control formulation of the problem of estimating the convex envelope of a non-convex function. Based on this, we develop a reinforcement learning scheme to approximate the convex envelope, using a variant of Q-learning for controlled optimal stopping. It shows very promising results on a standard library of test problems.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Convex Q Learning in a Stochastic Environment: Extended Version

86% match2023

Inverse Reinforcement Learning via Convex Optimization

82% match1 repo22025

A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms

81% match2024

Learning Safety Constraints from Demonstrations with Unknown Rewards

80% match1 repo72023

Deep Reinforcement Learning: A Convex Optimization Approach

80% match2024

A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation

79% match2026

Cite this paper

@article{borkar2023approximation,
  title  = {Approximation of Convex Envelope Using Reinforcement Learning},
  author = {Vivek S. Borkar and Adit Akarsh},
  year   = {2023},
  eprint = {2311.14421},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.14421v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2311.14421.svg)](https://codeofpaper.com/paper/2311.14421)

Discussion