  Rebalancing is a critical service bottleneck for many transportation
services, such as Citi Bike. Citi Bike relies on manual orchestrations of
rebalancing bikes between dispatchers and field agents. Motivated by such
problem and the lack of smart autonomous solutions in this area, this project
explored a new RL architecture called Distributed RL (DiRL) with Transfer
Learning (TL) capability. The DiRL solution is adaptive to changing traffic
dynamics when keeping bike stock under control at the minimum cost. DiRL
achieved a 350% improvement in bike rebalancing autonomously and TL offered a
62.4% performance boost in managing an entire bike network. Lastly, a field
trip to the dispatch office of Chariot, a ride-sharing service, provided
insights to overcome challenges of deploying an RL solution in the real world.
