A new approach for structural credit assignment in distributed reinforcement learning systems
Yu Zhong, Guochang Gu, Zhang Rubo · 2004
Most existing algorithm for structural credit assignment are developed for competitive reinforcement learning systems. In competitive reinforcement learning system, agents are activated one by one, so there is only one active agent at a time and structural credit assignment could be implemented by some temporal credit assignment algorithms. In collaborated reinforcement learning systems, agents are activated simultaneously, so how to transform the global reinforcement signal fed back from the environment to a reinforcement vector is a crucial difficulty that could not be slide over. In this article, the first really feasible and efficient structural credit assignment difficulty in collaborated reinforcement learning systems is primarily solved. The experiments show that the algorithm converges very rapidly and the assignment result is quite satisfying.