Awesome Decision Making / Reinforcement Learning

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.

Note: Here is also a collection of research materials for interaction-aware trajectory (behavior) prediction.

RL & IRL & GAIL

• 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]

Autonomous Driving

• 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]

Simulator & Dataset

• CARLA: An Open Urban Driving Simulator, [paper]
• TORCS: The open racing car simulator, [paper]
• Comma.ai: Learning a Driving Simulator, [paper]
• NGSIM: US Highway 101 Dataset, [docs]