A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem

Understand

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.