Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search

Cooperative trajectory planning methods for automated vehicles, are capable to solve traffic scenarios that require a high degree of cooperation between traffic participants. In order for cooperative systems to integrate in human-centered traffic, it is important that the automated systems behave human-like, so that humans can anticipate the system's decisions. While Reinforcement Learning has made remarkable progress in solving the decision making part, it is non-trivial to parameterize a reward model that yields predictable actions. This work employs feature-based Maximum Entropy Inverse Reinforcement Learning in combination with Monte Carlo Tree Search to learn reward models that maximizes the likelihood of recorded multi-agent cooperative expert trajectories. The evaluation demonstrates that the approach is capable of recovering a reasonable reward model that mimics the expert and performs similar to a manually tuned baseline reward model.