Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search
Today's automated vehicles lack the ability to cooperate implicitly with others. This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments. Based on cooperative modeling of o...
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| creator | Kurzer, Karl Zhou, Chenyang Zöllner, J Marius |
| description | Today's automated vehicles lack the ability to cooperate implicitly with others. This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments. Based on cooperative modeling of other agents and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the state-action-values of each agent in a cooperative and decentralized manner, explicitly modeling the interdependence of actions between traffic participants. Macro-actions allow for temporal extension over multiple time steps and increase the effective search depth requiring fewer iterations to plan over longer horizons. Without predefined policies for macro-actions, the algorithm simultaneously learns policies over and within macro-actions. The proposed method is evaluated under several conflict scenarios, showing that the algorithm can achieve effective cooperative planning with learned macro-actions in heterogeneous environments. |
| doi_str_mv | 10.48550/arxiv.1807.09530 |
| format | Article |
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| subjects | Algorithms Automation Computer Science - Artificial Intelligence Computer simulation Monte Carlo simulation Planning Policies Search algorithms Searching |
| title | Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search |
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