# Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration

arXiv: https://arxiv.org/abs/1509.06824 · HTML: https://mlsanity.com/paper/1509.06824/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2015.

## Built on by (most cited first, 8 of 8)

- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Exploration in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2205.00824/) (2022)
- [Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications](https://mlsanity.com/paper/2211.08064/) (2022)
- [A Survey on Physics Informed Reinforcement Learning: Review and Open Problems](https://mlsanity.com/paper/2309.01909/) (2023)
- [Deep Learning for Embodied Vision Navigation: A Survey](https://mlsanity.com/paper/2108.04097/) (2021)
- [Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout](https://mlsanity.com/paper/2309.13508/) (2023)
- [Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learning](https://mlsanity.com/paper/2407.02217/) (2024)
- [On Physical Origins of Learning](https://mlsanity.com/paper/2310.02375/) (2023)

## Abstract (arXiv)

In this paper, we present a robotic model-based reinforcement learning method that combines ideas from model identification and model predictive control. We use a feature-based representation of the dynamics that allows the dynamics model to be fitted with a simple least squares procedure, and the features are identified from a high-level specification of the robot's morphology, consisting of the number and connectivity structure of its links. Model predictive control is then used to choose the actions under an optimistic model of the dynamics, which produces an efficient and goal-directed exploration strategy. We present real time experimental results on standard benchmark problems involving the pendulum, cartpole, and double pendulum systems. Experiments indicate that our method is able to learn a range of benchmark tasks substantially faster than the previous best methods. To evaluate our approach on a realistic robotic control task, we also demonstrate real time control of a simulated 7 degree of freedom arm.