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Found 4 papers, 3 papers with code
Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments
We show that graph neural networks are capable of handling scenarios with a varying number and order of vehicles during training and application.
Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving
If a policy can handle all counterfactual worlds well, it either has seen similar situations during training or it generalizes well and is deemed to be fit enough to be executed in the actual world.
Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization
By storing the state-history during reinforcement learning, it can be used for constraint checking and the optimizer can account for interactions.
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
