  As one of the solutions to the decentralized partially observable Markov
decision process (Dec-POMDP) problems, the value decomposition method has
achieved significant results recently. However, most value decomposition
methods require the fully observable state of the environment during training,
but this is not feasible in some scenarios where only incomplete and noisy
observations can be obtained. Therefore, we propose a novel value decomposition
framework, named State Inference for value DEcomposition (SIDE), which
eliminates the need to know the global state by simultaneously seeking
solutions to the two problems of optimal control and state inference. SIDE can
be extended to any value decomposition method to tackle partially observable
problems. By comparing with the performance of different algorithms in
StarCraft II micromanagement tasks, we verified that though without accessible
states, SIDE can infer the current state that contributes to the reinforcement
learning process based on past local observations and even achieve superior
results to many baselines in some complex scenarios.
