Efficient Reward Functions for Adaptive Multi-rover Systems

This paper addresses how efficient reward methods can be applied to multiple agents co-evolving in noisy and changing environments, under communication limitations. This problem is approached by “factoring” a global reward over all agents into agent-specific rewards that have two key properties: 1) agents maximizing their agentspecific rewards will tend to maximize the global reward, 2) an agent’s action has a large influence over its agent-specific reward allowing it to evolve quickly. Agents using these agent-specific rewards are tested in episodic and non-episodic, continuous-space multi-rover environment where rovers evolve to maximize a global reward function over all rovers. The environments are dynamic (i.e. changes over time) and can be noisy and can restrict communication between agents . We show that a control policy evolved using these agent-specific rewards outperforms global reward methods by up to 400%. More notably, in the presence of a larger number of rovers or rovers with noisy and communication limited sensors, the proposed method outperforms global reward by a higher percentage than in noise-free conditions with a small number of rovers.