  Finding feasible and collision-free paths for multiple nonlinear agents is
challenging in the decentralized scenarios due to limited available information
of other agents and complex dynamics constraints. In this paper, we propose a
fast multi-agent collision avoidance algorithm for general nonlinear agents
with continuous action space, where each agent observes only positions and
velocities of nearby agents. To reduce online computation, we first decompose
the multi-agent scenario and solve a two agents collision avoidance problem
using reinforcement learning (RL). When extending the trained policy to a
multi-agent problem, safety is ensured by introducing the optimal reciprocal
collision avoidance (ORCA) as linear constraints and the overall collision
avoidance action could be found through simple convex optimization. Most
existing RL-based multi-agent collision avoidance algorithms rely on the direct
control of agent velocities. In sharp contrasts, our approach is applicable to
general nonlinear agents. Realistic simulations based on nonlinear bicycle
agent models are performed with various challenging scenarios, indicating a
competitive performance of the proposed method in avoiding collisions,
congestion and deadlock with smooth trajectories.
