# Deep Apprenticeship Learning for Playing Games

arXiv: https://arxiv.org/abs/2205.07959 · HTML: https://mlsanity.com/paper/2205.07959/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 41 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 2 of 2)

- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [A Deep Architecture for Semantic Parsing](https://mlsanity.com/paper/1404.7296/) (2014)

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

- [Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning](https://mlsanity.com/paper/2511.20591/) (2025)

## Abstract (arXiv)

In the last decade, deep learning has achieved great success in machine learning tasks where the input data is represented with different levels of abstractions. Driven by the recent research in reinforcement learning using deep neural networks, we explore the feasibility of designing a learning model based on expert behaviour for complex, multidimensional tasks where reward function is not available. We propose a novel method for apprenticeship learning based on the previous research on supervised learning techniques in reinforcement learning. Our method is applied to video frames from Atari games in order to teach an artificial agent to play those games. Even though the reported results are not comparable with the state-of-the-art results in reinforcement learning, we demonstrate that such an approach has the potential to achieve strong performance in the future and is worthwhile for further research.