Deep Reinforcement Learning Based LeftTurn Connected and Automated Vehicle Control at Signalized Intersection in VehicletoInfrastructure Environment In order to solve the problem of vehicle delay caused by stops at signalized intersections, a microcontrol method of a leftturning connected and automated vehicle (CAV) based on an improved deep deterministic policy gradient (DDPG) is designed in this paper. In this paper, the microcontrol of the whole process of a leftturn vehicle approaching, entering, and leaving a signalized intersection is considered. In addition, in order to solve the problems of low sampling efficiency and overestimation of the critic network of the DDPG algorithm, a positive and negative reward experience replay buffer sampling mechanism and multicritic network structure are adopted in the DDPG algorithm in this paper. Finally, the effectiveness of the signal control method, six DDPGbased methods (DDPG, PNRERB1CDDPG, PNRERB3CDDPG, PNRERB5CDDPG, PNRERB5CNGDDPG, and PNRERB7CDDPG), and four DQNbased methods (DQN, Dueling DQN, Double DQN, and Prioritized Replay DQN) are verified under 0.2, 0.5, and 0.7 saturation degrees of leftturning vehicles at a signalized intersection within a VISSIM simulation environment. The results show that the proposed deep reinforcement learning method can get a number of stops benefits ranging from 5% to 94%, stop time benefits ranging from 1% to 99%, and delay benefits ranging from xe2x80x9317% to 93%, respectively compared with the traditional signal control method.

Learning Adaptive Driving Behavior Using Recurrent Deterministic Policy Gradients In this work, we propose adaptive driving behaviors for simulated cars using continuous control deep reinforcement learning. Deep Deterministic Policy Gradient(DDPG) is known to give smooth driving maneuvers in simulated environments. Unfortunately, simple feedforward networks, lack the capability to contain temporal information, hence we have used its Recurrent variant called Recurrent Deterministic Policy Gradients. Our trained agent adapts itself to the velocity of the traffic. It is capable of slowing down in the presence of dense traffic, to prevent collisions as well to speed up and change lanes in order to overtake when the traffic is sparse. The reasons for the above behavior, as well as, our main contributions are: 1. Application of Recurrent Deterministic Policy Gradients. 2. Novel reward function formulation. 3. Modified Replay Buffer called Near and Far Replay Buffers, wherein we maintain two replay buffers and sample equally from both of them.