# Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2102.06765 · HTML: https://mlsanity.com/paper/2102.06765/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/1705.01196/) (2017)
- [Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning](https://mlsanity.com/paper/2004.04450/) (2020)
- [Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments](https://mlsanity.com/paper/1904.11483/) (2019)
- [Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning](https://mlsanity.com/paper/1810.10469/) (2018)
- [Belief State Planning for Autonomously Navigating Urban Intersections](https://mlsanity.com/paper/1704.04322/) (2017)
- [Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States](https://mlsanity.com/paper/1809.03214/) (2018)
- [Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search](https://mlsanity.com/paper/2002.00497/) (2020)
- [Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously Handling Multiple Intersections](https://mlsanity.com/paper/1705.01197/) (2017)

## Built on by (most cited first, 3 of 3)

- [Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search](https://mlsanity.com/paper/2202.06443/) (2022)
- [Enhancing Safety for Autonomous Agents in Partly Concealed Urban Traffic Environments Through Representation-Based Shielding](https://mlsanity.com/paper/2407.04343/) (2024)
- [Realistic adversarial scenario generation via human-like pedestrian model for autonomous vehicle control parameter optimisation](https://mlsanity.com/paper/2601.02082/) (2026)

## Abstract (arXiv)

Data driven approaches for decision making applied to automated driving require appropriate generalization strategies, to ensure applicability to the world's variability. Current approaches either do not generalize well beyond the training data or are not capable to consider a variable number of traffic participants. Therefore we propose an invariant environment representation from the perspective of the ego vehicle. The representation encodes all necessary information for safe decision making. To assess the generalization capabilities of the novel environment representation, we train our agents on a small subset of scenarios and evaluate on the entire diverse set of scenarios. Here we show that the agents are capable to generalize successfully to unseen scenarios, due to the abstraction. In addition we present a simple occlusion model that enables our agents to navigate intersections with occlusions without a significant change in performance.