A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
Learning multimodal rewards from rankings
Learning from human feedback has shown to be a useful approach in acquiring robot
reward functions. However, expert feedback is often assumed to be drawn from an …
reward functions. However, expert feedback is often assumed to be drawn from an …
Deep generative models for offline policy learning: Tutorial, survey, and perspectives on future directions
Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …
particularly in generating texts, images, and videos using models trained from offline data …
Ess-infogail: Semi-supervised imitation learning from imbalanced demonstrations
Imitation learning aims to reproduce expert behaviors without relying on an explicit reward
signal. However, real-world demonstrations often present challenges, such as multi-modal …
signal. However, real-world demonstrations often present challenges, such as multi-modal …
Adversarial option-aware hierarchical imitation learning
It has been a challenge to learning skills for an agent from long-horizon unannotated
demonstrations. Existing approaches like Hierarchical Imitation Learning (HIL) are prone to …
demonstrations. Existing approaches like Hierarchical Imitation Learning (HIL) are prone to …
Lane change decision prediction: an efficient BO-XGB modelling approach with SHAP analysis
The lane-change decision (LCD) is a critical aspect of driving behaviour. This study
proposes an LCD model based on a Bayesian optimization (BO) framework and extreme …
proposes an LCD model based on a Bayesian optimization (BO) framework and extreme …
A dynamic test scenario generation method for autonomous vehicles based on conditional generative adversarial imitation learning
L Jia, D Yang, Y Ren, C Qian, Q Feng, B Sun… - Accident Analysis & …, 2024 - Elsevier
Autonomous vehicles must be comprehensively evaluated before deployed in cities and
highways. However, most existing evaluation approaches for autonomous vehicles are static …
highways. However, most existing evaluation approaches for autonomous vehicles are static …
Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review
Y Wang, M Hayashibe, D Owaki - Applied Sciences, 2024 - mdpi.com
Policy learning enables agents to learn how to map states to actions, thus enabling adaptive
and flexible behavioral generation in complex environments. Policy learning methods are …
and flexible behavioral generation in complex environments. Policy learning methods are …
RTA-IR: A runtime assurance framework for behavior planning based on imitation learning and responsibility-sensitive safety model
Y Peng, G Tan, H Si - Expert Systems with Applications, 2023 - Elsevier
Current research on artificial intelligence (AI) algorithms in safety–critical areas remains
extremely challenging due to their inability to be fully verified at design time. In this paper …
extremely challenging due to their inability to be fully verified at design time. In this paper …
Hierarchical Imitation Learning for Stochastic Environments
M Igl, P Shah, P Mougin, S Srinivasan… - 2023 IEEE/RSJ …, 2023 - ieeexplore.ieee.org
Many applications of imitation learning require the agent to generate the full distribution of
behaviour observed in the training data. For example, to evaluate the safety of autonomous …
behaviour observed in the training data. For example, to evaluate the safety of autonomous …