# Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions

arXiv: https://arxiv.org/abs/2402.04168 · HTML: https://mlsanity.com/paper/2402.04168/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 21 of 21)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [Reward (Mis)design for Autonomous Driving](https://mlsanity.com/paper/2104.13906/) (2021)
- [Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks](https://mlsanity.com/paper/1902.09355/) (2019)
- [End-to-End Race Driving with Deep Reinforcement Learning](https://mlsanity.com/paper/1807.02371/) (2018)
- [Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.10056/) (2018)
- [Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning](https://mlsanity.com/paper/2002.02667/) (2020)
- [End-to-End Deep Reinforcement Learning for Lane Keeping Assist](https://mlsanity.com/paper/1612.04340/) (2016)
- [Autonomous Highway Driving using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.00035/) (2019)
- [One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving](https://mlsanity.com/paper/2209.00342/) (2022)
- [Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios](https://mlsanity.com/paper/1904.06025/) (2019)
- [Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic](https://mlsanity.com/paper/2005.11895/) (2020)
- [Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States](https://mlsanity.com/paper/1809.03214/) (2018)
- [Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2306.15968/) (2023)
- [Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic](https://mlsanity.com/paper/1902.05772/) (2019)
- [Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces](https://mlsanity.com/paper/2110.00044/) (2021)
- [Safe Trajectory Planning Using Reinforcement Learning for Self Driving](https://mlsanity.com/paper/2011.04702/) (2020)
- [Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules](https://mlsanity.com/paper/2202.09269/) (2022)
- [A Fuzzy Approach to Qualification in Design Exploration for Autonomous Robots and Systems](https://mlsanity.com/paper/1606.01077/) (2016)

## Built on by (most cited first, 4 of 4)

- [Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning](https://mlsanity.com/paper/2505.08264/) (2025)
- [Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving](https://mlsanity.com/paper/2505.06737/) (2025)
- [An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving](https://mlsanity.com/paper/2409.06607/) (2024)
- [Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving](https://mlsanity.com/paper/2603.20230/) (2026)

## Abstract (arXiv)

Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. Common approaches use non-interpretable control commands as the action space and unstructured reward designs which lack structure. In this work, we introduce Informed Reinforcement Learning, where a structured rulebook is integrated as a knowledge source. We learn trajectories and asses them with a situation-aware reward design, leading to a dynamic reward which allows the agent to learn situations which require controlled traffic rule exceptions. Our method is applicable to arbitrary RL models. We successfully demonstrate high completion rates of complex scenarios with recent model-based agents.