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Found 4 papers, 1 papers with code
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning
With Deep Reinforcement Learning, optimal driving strategies for such problems can be derived also for higher-dimensional problems.
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning
Specifically, we first learn an optimal policy in an uncertain environment with Deep Distributional Reinforcement Learning.
Distributional Reinforcement Learning
Reinforcement Learning (RL)
Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving
In this work, we adopt this safety objective for interactive planning.
BARK: Open Behavior Benchmarking in Multi-Agent Environments
As driving tests are costly and challenging scenarios are hard to find and reproduce, simulation is widely used to develop, test, and benchmark behavior models.
Multiagent Systems
