jiachenli94 / Awesome Decision Making Reinforcement Learning
Licence: mit
A selection of state-of-the-art research materials on decision making and motion planning.
Stars: ✭ 68
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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: [email protected]
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
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