Resolving the Perceptual Aliasing Problem by Multi-Agent Reinforcement Learning

Perceptual aliasing occurs when distinct environmental states generate the same observation even though different actions may be appropriate. This paper studies whether the observable presence of other agents can mitigate such ambiguity without adding an explicit communication channel or internal memory. We formalize other-agent perception as a refinement of the focal agent’s observation and characterize when the resulting augmented observations are action-consistent. The analysis distinguishes local resolution of observation-induced action conflicts from the stronger requirements needed for a global goal-reaching policy. We evaluate the mechanism with Profit Sharing and Q-learning in three grid environments and under variations in agent number, execution protocol, and sensor noise and compare it with a one-step-history baseline. The results show that other-agent cues can be useful when they create action-relevant distinctions, but they may also increase representation cost or fail when informative configurations are rare or unstable. Statistical comparisons treat non-terminating trials as right-censored observations rather than completed runs. Overall, the results support a conditional conclusion: other agents can provide useful perceptual information, but the benefit depends on environment structure, learner dynamics, action selection, and the temporal availability of the cue.