1 research outputs found
Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles
Reinforcement Learning algorithms have recently been proposed to learn
time-sequential control policies in the field of autonomous driving. Direct
applications of Reinforcement Learning algorithms with discrete action space
will yield unsatisfactory results at the operational level of driving where
continuous control actions are actually required. In addition, the design of
neural networks often fails to incorporate the domain knowledge of the
targeting problem such as the classical control theories in our case. In this
paper, we propose a hybrid model by combining Q-learning and classic PID
(Proportion Integration Differentiation) controller for handling continuous
vehicle control problems under dynamic driving environment. Particularly,
instead of using a big neural network as Q-function approximation, we design a
Quadratic Q-function over actions with multiple simple neural networks for
finding optimal values within a continuous space. We also build an action
network based on the domain knowledge of the control mechanism of a PID
controller to guide the agent to explore optimal actions more efficiently.We
test our proposed approach in simulation under two common but challenging
driving situations, the lane change scenario and ramp merge scenario. Results
show that the autonomous vehicle agent can successfully learn a smooth and
efficient driving behavior in both situations.Comment: Machine Learning for Autonomous Driving Workshop on NeurIPS, 201