  For multi-agent reinforcement learning systems (MARLS), the problem
formulation generally involves investing massive reward engineering effort
specific to a given problem. However, this effort often cannot be translated to
other problems; worse, it gets wasted when system dynamics change drastically.
This problem is further exacerbated in sparse reward scenarios, where a
meaningful heuristic can assist in the policy convergence task. We propose
GOVerned Reward Engineering Kernels (GOV-REK), which dynamically assign reward
distributions to agents in MARLS during its learning stage. We also introduce
governance kernels, which exploit the underlying structure in either state or
joint action space for assigning meaningful agent reward distributions. During
the agent learning stage, it iteratively explores different reward distribution
configurations with a Hyperband-like algorithm to learn ideal agent reward
models in a problem-agnostic manner. Our experiments demonstrate that our
meaningful reward priors robustly jumpstart the learning process for
effectively learning different MARL problems.
