# Delta Schema Network in Model-based Reinforcement Learning

arXiv: https://arxiv.org/abs/2006.09950 · HTML: https://mlsanity.com/paper/2006.09950/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments](https://mlsanity.com/paper/2108.06148/) (2021)

## Abstract (arXiv)

This work is devoted to unresolved problems of Artificial General Intelligence - the inefficiency of transfer learning. One of the mechanisms that are used to solve this problem in the area of reinforcement learning is a model-based approach. In the paper we are expanding the schema networks method which allows to extract the logical relationships between objects and actions from the environment data. We present algorithms for training a Delta Schema Network (DSN), predicting future states of the environment and planning actions that will lead to positive reward. DSN shows strong performance of transfer learning on the classic Atari game environment.