# Internal Model from Observations for Reward Shaping

arXiv: https://arxiv.org/abs/1806.01267 · HTML: https://mlsanity.com/paper/1806.01267/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2018.

## Built on by (most cited first, 11 of 11)

- [Recent Advances in Imitation Learning from Observation](https://mlsanity.com/paper/1905.13566/) (2019)
- [Neuro-Symbolic Reinforcement Learning with First-Order Logic](https://mlsanity.com/paper/2110.10963/) (2021)
- [Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks](https://mlsanity.com/paper/2107.05825/) (2021)
- [Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization](https://mlsanity.com/paper/2203.02214/) (2022)
- [Reinforcement Learning with External Knowledge by using Logical Neural Networks](https://mlsanity.com/paper/2103.02363/) (2021)
- [Improving Policy Learning via Language Dynamics Distillation](https://mlsanity.com/paper/2210.00066/) (2022)
- [LOA: Logical Optimal Actions for Text-based Interaction Games](https://mlsanity.com/paper/2110.10973/) (2021)
- [Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning](https://mlsanity.com/paper/2412.00086/) (2024)
- [Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning](https://mlsanity.com/paper/1810.01108/) (2018)
- [Commonsense Knowledge from Scene Graphs for Textual Environments](https://mlsanity.com/paper/2210.14162/) (2022)
- [Imitation from Observation With Bootstrapped Contrastive Learning](https://mlsanity.com/paper/2302.06540/) (2023)

## Abstract (arXiv)

Reinforcement learning methods require careful design involving a reward function to obtain the desired action policy for a given task. In the absence of hand-crafted reward functions, prior work on the topic has proposed several methods for reward estimation by using expert state trajectories and action pairs. However, there are cases where complete or good action information cannot be obtained from expert demonstrations. We propose a novel reinforcement learning method in which the agent learns an internal model of observation on the basis of expert-demonstrated state trajectories to estimate rewards without completely learning the dynamics of the external environment from state-action pairs. The internal model is obtained in the form of a predictive model for the given expert state distribution. During reinforcement learning, the agent predicts the reward as a function of the difference between the actual state and the state predicted by the internal model. We conducted multiple experiments in environments of varying complexity, including the Super Mario Bros and Flappy Bird games. We show our method successfully trains good policies directly from expert game-play videos.