This is a paper list of state-of-the-art research materials related to decision making and motion planning. Wish it could be helpful for both academia and industry. (Still updating)
Maintainers: Jiachen Li (University of California, Berkeley)
Email: jiachen_li@berkeley.edu
Please feel free to pull request to add new resources or send emails to us for questions, discussion and collaborations.
Also welcome to check the current research in our MSC Lab at UC Berkeley.
Research Intern: Please read this if you want to apply for research intern opportunities in our group.
Note: Here is also a collection of research materials for interaction-aware trajectory (behavior) prediction.
- Maximum Entropy Deep Inverse Reinforcement Learning, 2015, [paper]
- Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, ICML 2016, [paper]
- Generative Adversarial Imitation Learning, NIPS 2016, [paper]
- A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models, NIPS 2016, [paper]
- InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations, NIPS 2017, [paper] [code]
- Self-Imitation Learning, ICML 2018, [paper] [code]
- Data-Efficient Hierarchical Reinforcement Learning, NIPS 2018, [paper]
- Learning Robust Rewards with Adversarial Inverse Reinforcement Learning, ICLR 2018, [paper]
- Multi-Agent Generative Adversarial Imitation Learning, ICLR 2018, [paper]
- Multi-Agent Adversarial Inverse Reinforcement Learning, ICML 2019, [paper]
- A Survey of Deep Learning Applications to Autonomous Vehicle Control, IEEE Transaction on ITS 2019, [paper]
- Imitating Driver Behavior with Generative Adversarial Networks, IV 2017, [paper] [code]
- Multi-Agent Imitation Learning for Driving Simulation, IROS 2018, [paper] [code]
- Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning, ICRA 2019, [paper] [code]
- Learning from Demonstration in the Wild, ICRA 2018, [paper]
- Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning, NeurIPS 2019, [paper] [code]
- Model-free Deep Reinforcement Learning for Urban Autonomous Driving, ITSC 2019, [paper]
- End-to-end driving via conditional imitation learning, ICRA 2018, [paper]
- CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving, ECCV 2018, [paper] [code]
- A reinforcement learning based approach for automated lane change maneuvers, IV 2018, [paper]
- Adversarial Inverse Reinforcement Learning for Decision Making in Autonomous Driving, ICRA 2020, [paper]
- Deep hierarchical reinforcement learning for autonomous driving with distinct behaviors, IV 2018, [paper]
- A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning, ICML 2019, [paper]
- End-to-end Interpretable Neural Motion Planner, CVPR 2019, [paper]
- Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles, IROS 2019, [paper]
- Dynamic Input for Deep Reinforcement Learning in Autonomous Driving, IROS 2019, [paper]
- Learning to Navigate in Cities Without a Map, NIPS 2018, [paper]
- Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation, NIPS 2018, [paper]
- Towards Learning Multi-agent Negotiations via Self-Play, ICCV 2019, [paper]