Attention-Based Recurrency for Multi-Agent Reinforcement Learning under State Uncertainty

State uncertainty poses a major challenge for decentralized coordination. However, state uncertainty is largely neglected in multiagent reinforcement learning research due to a strong focus on state-based centralized training for decentralized execution (CTDE) and benchmarks that lack sufficient stochasticity like StarCraft Multi-Agent Challenge (SMAC). In this work, we propose Attentionbased Embeddings of Recurrence In multi-Agent Learning (AERIAL) to approximate value functions under agent-wise state uncertainty. AERIAL uses a learned representation of multi-agent recurrence, considering more accurate information about decentralized agent decisions than state-based CTDE. We then introduce MessySMAC, a modified version of SMAC with stochastic observations and higher variance in initial states, to provide a more general and configurable benchmark. We evaluate AERIAL in a variety of MessySMAC maps, and compare the results with state-based CTDE.