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We present a novel method to address the problem of multi-vehicle conflictresolution in highly constrained spaces. An optimal control problem isformulated to incorporate nonlinear, non-holonomic vehicle dynamics and exactcollision avoidance constraints. A solution to the problem can be obtained byfirst learning configuration strategies with reinforcement learning (RL) in asimplified discrete environment, and then using these strategies to shape theconstraint space of the original problem. Simulation results show that ourmethod can explore efficient actions to resolve conflicts in confined space andgenerate dexterous maneuvers that are both collision-free and kinematicallyfeasible.

Multi-vehicle Conflict Resolution in Highly Constrained Spaces by Merging Optimal Control and Reinforcement Learning