Policy Invariance under Reward Transformations for General-Sum   Stochastic Games
  We extend the potential-based shaping method from Markov decision processes to multi-player general-sum stochastic games. We prove that the Nash equilibria in a stochastic game remains unchanged after potential-based shaping is applied to the environment. The property of policy invariance provides a possible way of speeding convergence when learning to play a stochastic game. 
Xiaosong Lu, Howard M. Schwartz, Sidney N. Givigi Jr
cs.GT cs.LG