Hierarchical Program-Triggered Reinforcement Learning Agents for Automated Driving
Explore this paper's citation graph
- Type
- article
- Published
- 2021-03-25
- Cited by
- 50
- References
- 46
- Access
- Open access
- OpenAlex
- https://openalex.org/W3137786033
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:232352854
Keywords
Reinforcement learning, Interpretability, Computer science, Bottleneck, Task (project management)
References
- Principles of model checking
- Recent Advances in Hierarchical Reinforcement Learning
- Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes
- Temporal Logic of Programs
- Maneuver-Based Trajectory Planning for Highly Autonomous Vehicles on Real Road With Traffic and Driver Interaction
- Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions
- Mastering the game of Go with deep neural networks and tree search
- Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
- End to End Learning for Self-Driving Cars
- Concrete Problems in AI Safety
- A synthesis of automated planning and reinforcement learning for efficient, robust decision-making
- Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
- Deep Reinforcement Learning framework for Autonomous Driving
- End-to-End Driving in a Realistic Racing Game with Deep Reinforcement Learning
- On a Formal Model of Safe and Scalable Self-driving Cars
- Tactical Decision Making for Lane Changing with Deep Reinforcement Learning
- PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making
- Learning to Drive in a Day
- Human-like Autonomous Vehicle Speed Control by Deep Reinforcement Learning with Double Q-Learning
- Markov probabilistic decision making of self-driving cars in highway with random traffic flow: a simulation study
Cited by
- A Survey of Deep RL and IL for Autonomous Driving Policy Learning
- Compositional Learning and Verification of Neural Network Controllers
- LanCon-Learn: Learning with Language to Enable Generalization in Multi-Task Manipulation
- A MADDPG-based multi-agent antagonistic algorithm for sea battlefield confrontation
- Safe and Stable RL (S2RL) Driving Policies Using Control Barrier and Control Lyapunov Functions
- Graph Reinforcement Learning Application to Co-operative Decision-Making in Mixed Autonomy Traffic: Framework, Survey, and Challenges
- Cola-HRL: Continuous-Lattice Hierarchical Reinforcement Learning for Autonomous Driving
- A Hybrid Driving Decision-Making System Integrating Markov Logic Networks and Connectionist AI
- Safe Robot Navigation Using Constrained Hierarchical Reinforcement Learning
- EnsembleFollower: A Hybrid Car-Following Framework Based On Reinforcement Learning and Hierarchical Planning
- Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles
- Machine Learning Meets Advanced Robotic Manipulation
- Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends
- Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach with Safety Guarantees
- Strategy-Following Multi-Agent Deep Reinforcement Learning through External High-Level Instruction
- Joint Imitation Learning of Behavior Decision and Control for Autonomous Intersection Navigation
- Safe and Robust Reinforcement Learning: Principles and Practice
- Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
- A novel deep ensemble reinforcement learning based control method for strip flatness in cold rolling steel industry
- Cooperative Merging Control Based on Reinforcement Learning With Dynamic Waypoint
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