# Randomized Adversarial Imitation Learning for Autonomous Driving

arXiv: https://arxiv.org/abs/1905.05637 · HTML: https://mlsanity.com/paper/1905.05637/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 13 May 2019.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning](https://mlsanity.com/paper/1708.02596/) (2017)
- [Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning](https://mlsanity.com/paper/1809.02925/) (2018)
- [A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models](https://mlsanity.com/paper/1611.03852/) (2016)
- [Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control](https://mlsanity.com/paper/1708.04133/) (2017)
- [Simple random search provides a competitive approach to reinforcement learning](https://mlsanity.com/paper/1803.07055/) (2018)
- [Towards Generalization and Simplicity in Continuous Control](https://mlsanity.com/paper/1703.02660/) (2017)
- [OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1709.06683/) (2017)
- [Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.10056/) (2018)
- [End-to-End Deep Reinforcement Learning for Lane Keeping Assist](https://mlsanity.com/paper/1612.04340/) (2016)
- [Generative Adversarial Self-Imitation Learning](https://mlsanity.com/paper/1812.00950/) (2018)

## Built on by (most cited first, 6 of 6)

- [Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games](https://mlsanity.com/paper/2105.10211/) (2021)
- [Adversarial Imitation Learning via Random Search in Lane Change Decision-Making](https://mlsanity.com/paper/2109.05197/) (2021)
- [Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles](https://mlsanity.com/paper/2112.14710/) (2021)
- [Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks](https://mlsanity.com/paper/2209.00862/) (2022)
- [Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception](https://mlsanity.com/paper/2211.03502/) (2022)
- [RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learning](https://mlsanity.com/paper/2407.02520/) (2024)

## Abstract (arXiv)

With the evolution of various advanced driver assistance system (ADAS) platforms, the design of autonomous driving system is becoming more complex and safety-critical. The autonomous driving system simultaneously activates multiple ADAS functions; and thus it is essential to coordinate various ADAS functions. This paper proposes a randomized adversarial imitation learning (RAIL) method that imitates the coordination of autonomous vehicle equipped with advanced sensors. The RAIL policies are trained through derivative-free optimization for the decision maker that coordinates the proper ADAS functions, e.g., smart cruise control and lane keeping system. Especially, the proposed method is also able to deal with the LIDAR data and makes decisions in complex multi-lane highways and multi-agent environments.