  With the growing need to reduce energy consumption and greenhouse gas
emissions, Eco-driving strategies provide a significant opportunity for
additional fuel savings on top of other technological solutions being pursued
in the transportation sector. In this paper, a model-free deep reinforcement
learning (RL) control agent is proposed for active Eco-driving assistance that
trades-off fuel consumption against other driver-accommodation objectives, and
learns optimal traction torque and transmission shifting policies from
experience. The training scheme for the proposed RL agent uses an off-policy
actor-critic architecture that iteratively does policy evaluation with a
multi-step return and policy improvement with the maximum posteriori policy
optimization algorithm for hybrid action spaces. The proposed Eco-driving RL
agent is implemented on a commercial vehicle in car following traffic. It shows
superior performance in minimizing fuel consumption compared to a baseline
controller that has full knowledge of fuel-efficiency tables.
