# Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously Handling Multiple Intersections

arXiv: https://arxiv.org/abs/1705.01197 · HTML: https://mlsanity.com/paper/1705.01197/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2017.

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

- [Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.06765/) (2021)

## Abstract (arXiv)

We analyze how the knowledge to autonomously handle one type of intersection, represented as a Deep Q-Network, translates to other types of intersections (tasks). We view intersection handling as a deep reinforcement learning problem, which approximates the state action Q function as a deep neural network. Using a traffic simulator, we show that directly copying a network trained for one type of intersection to another type of intersection decreases the success rate. We also show that when a network that is pre-trained on Task A and then is fine-tuned on a Task B, the resulting network not only performs better on the Task B than an network exclusively trained on Task A, but also retained knowledge on the Task A. Finally, we examine a lifelong learning setting, where we train a single network on five different types of intersections sequentially and show that the resulting network exhibited catastrophic forgetting of knowledge on previous tasks. This result suggests a need for a long-term memory component to preserve knowledge.