  We present a computational framework for synthesis of distributed control
strategies for a heterogeneous team of robots in a partially observable
environment. The goal is to cooperatively satisfy specifications given as
Truncated Linear Temporal Logic (TLTL) formulas. Our approach formulates the
synthesis problem as a stochastic game and employs a policy graph method to
find a control strategy with memory for each agent. We construct the stochastic
game on the product between the team transition system and a finite state
automaton (FSA) that tracks the satisfaction of the TLTL formula. We use the
quantitative semantics of TLTL as the reward of the game, and further reshape
it using the FSA to guide and accelerate the learning process. Simulation
results demonstrate the efficacy of the proposed solution under demanding task
specifications and the effectiveness of reward shaping in significantly
accelerating the speed of learning.
