# Reinforcement Learning with Partial Parametric Model Knowledge

arXiv: https://arxiv.org/abs/2304.13223 · HTML: https://mlsanity.com/paper/2304.13223/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [Mastering Atari Games with Limited Data](https://mlsanity.com/paper/2111.00210/) (2021)
- [Parameter Space Noise for Exploration](https://mlsanity.com/paper/1706.01905/) (2017)
- [Value Iteration Networks](https://mlsanity.com/paper/1602.02867/) (2016)
- [On the Sample Complexity of the Linear Quadratic Regulator](https://mlsanity.com/paper/1710.01688/) (2017)
- [Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator](https://mlsanity.com/paper/1712.08642/) (2017)
- [Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator](https://mlsanity.com/paper/1905.12842/) (2019)
- [Deep Reinforcement Learning for Process Control: A Primer for Beginners](https://mlsanity.com/paper/2004.05490/) (2020)
- [Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach](https://mlsanity.com/paper/2203.09661/) (2022)

## Abstract (arXiv)

We adapt reinforcement learning (RL) methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment. Our method, Partial Knowledge Least Squares Policy Iteration (PLSPI), takes inspiration from both model-free RL and model-based control. It uses incomplete information from a partial model and retains RL's data-driven adaption towards optimal performance. The linear quadratic regulator provides a case study; numerical experiments demonstrate the effectiveness and resulting benefits of the proposed method.