Precise Control for Deep Driving using Dual Critic based DRL Approaches

Autonomous driving problems related to vehicle control using deep reinforcement learning (DRL) techniques, are still unsolved. DRL approaches have achieved notable results, its dependability on reward functions and defining the type of control actions are dominating factors of the objective, that controls its success. Several DRL approaches applied in the past consider a finite set of available actions to be controlled by the agent hence, it performs sharp actions. While real driving requires precision control capabilities that tend to apply safer and smoother actions. For incorporating such precision control capabilities, this paper considers the driving problem as a continuous control problem. For this, the gym-highway environments are used as these environments are controllable and customizable to simulate diverse driving scenarios. The simulation setup for parking is updated to resemble the complex scenario and for highway driving a novel reward function is designed to handle continuous actions. Dual critic based DRL approaches are applied as these approaches have shown remarkable performance in robotic locomotion control problems. The video results demonstrate the way different policies fulfil the objective.