# Co-training for Policy Learning

arXiv: https://arxiv.org/abs/1907.04484 · HTML: https://mlsanity.com/paper/1907.04484/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [A Survey for Solving Mixed Integer Programming via Machine Learning](https://mlsanity.com/paper/2203.02878/) (2022)
- [Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning](https://mlsanity.com/paper/2111.10810/) (2021)
- [Guided Policy Optimization under Partial Observability](https://mlsanity.com/paper/2505.15418/) (2025)
- [Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems](https://mlsanity.com/paper/2102.03002/) (2021)
- [Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints](https://mlsanity.com/paper/2308.12680/) (2023)
- [Minimize Control Inputs for Strong Structural Controllability Using Reinforcement Learning with Graph Neural Network](https://mlsanity.com/paper/2402.16925/) (2024)

## Abstract (arXiv)

We study the problem of learning sequential decision-making policies in settings with multiple state-action representations. Such settings naturally arise in many domains, such as planning (e.g., multiple integer programming formulations) and various combinatorial optimization problems (e.g., those with both integer programming and graph-based formulations). Inspired by the classical co-training framework for classification, we study the problem of co-training for policy learning. We present sufficient conditions under which learning from two views can improve upon learning from a single view alone. Motivated by these theoretical insights, we present a meta-algorithm for co-training for sequential decision making. Our framework is compatible with both reinforcement learning and imitation learning. We validate the effectiveness of our approach across a wide range of tasks, including discrete/continuous control and combinatorial optimization.