Multi-vehicle cooperative control in ramp merging area based on MADDPG algorithm
A multi-vehicle cooperative control method based on the multi-agent reinforcement learning algorithm was proposed to ensure the safety and efficiency of the ramp merging area.A distributed training framework based on the Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm was designed with the goal of enhancing the computational efficiency of the system;In response to the challenge of the agent model dealing with continuous traffic flow scenarios,the stability of the agent towards the continuous traffic flow environment was guaranteed by constructing a relatively stationary environment and improving the strategy update gradient.The ramp merging area scenario was split into a preparation area and an entry area,and according to the control objectives of the two areas,the state and action spaces and reward functions were designed separately.The results show that,under different traffic flows,the proposed method reduces the total delay time in the merging area by an average of 25.46%comparing with the rule-based method,the delay time difference is 8.47%comparing with the global optimization method,but the control duration does not increase with the number of vehicles.Therefore,the proposed multi-vehicle cooperative control method for the ramp merging area can better balance the improvement of traffic efficiency and the real-time performance of the system.
multi-agent deep deterministic policy gradient(MADDPG)multi-agent reinforcement learningmulti-vehicle cooperative controlramp merging
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