  We present H-TD2: Hybrid Temporal Difference Learning for Taxi Dispatch, a
model-free, adaptive decision-making algorithm to coordinate a large fleet of
automated taxis in a dynamic urban environment to minimize expected customer
waiting times. Our scalable algorithm exploits the natural transportation
network company topology by switching between two behaviors: distributed
temporal-difference learning computed locally at each taxi and infrequent
centralized Bellman updates computed at the dispatch center. We derive a regret
bound and design the trigger condition between the two behaviors to explicitly
control the trade-off between computational complexity and the individual taxi
policy's bounded sub-optimality; this advances the state of the art by enabling
distributed operation with bounded-suboptimality. Additionally, unlike recent
reinforcement learning dispatch methods, this policy estimation is adaptive and
robust to out-of-training domain events. This result is enabled by a two-step
modelling approach: the policy is learned on an agent-agnostic, cell-based
Markov Decision Process and individual taxis are coordinated using the learned
policy in a distributed game-theoretic task assignment. We validate our
algorithm against a receding horizon control baseline in a Gridworld
environment with a simulated customer dataset, where the proposed solution
decreases average customer waiting time by 50% over a wide range of parameters.
We also validate in a Chicago city environment with real customer requests from
the Chicago taxi public dataset where the proposed solution decreases average
customer waiting time by 26% over irregular customer distributions during a
2016 Major League Baseball World Series game.
