# Dynamic Input for Deep Reinforcement Learning in Autonomous Driving

## TL;DR

This paper explores how Deep Sets can improve deep reinforcement learning for autonomous driving by effectively handling variable numbers of surrounding objects, leading to better performance and generalization.

## Contribution

It demonstrates that Deep Sets outperform traditional neural network architectures in processing variable-sized inputs in reinforcement learning for autonomous driving.

## Key findings

- Deep Sets achieve superior overall performance.
- Deep Sets generalize better to unseen scenarios.
- Traditional architectures face limitations with variable input sizes.

## Abstract

In many real-world decision making problems, reaching an optimal decision requires taking into account a variable number of objects around the agent. Autonomous driving is a domain in which this is especially relevant, since the number of cars surrounding the agent varies considerably over time and affects the optimal action to be taken. Classical methods that process object lists can deal with this requirement. However, to take advantage of recent high-performing methods based on deep reinforcement learning in modular pipelines, special architectures are necessary. For these, a number of options exist, but a thorough comparison of the different possibilities is missing. In this paper, we elaborate limitations of fully-connected neural networks and other established approaches like convolutional and recurrent neural networks in the context of reinforcement learning problems that have to deal with variable sized inputs. We employ the structure of Deep Sets in off-policy reinforcement learning for high-level decision making, highlight their capabilities to alleviate these limitations, and show that Deep Sets not only yield the best overall performance but also offer better generalization to unseen situations than the other approaches.