Presentation
in
Affinity Event: Indigenous in AI/ML
Mācistan: Reciprocity in Multi-Agent Reinforcement Learning as a Credit Assignment Problem
Dane Malenfant
Abstract:
Humans and animals have complex social and cultural systems that can span large distances and times. In North America, Plains Indigenous nations like the Métis Nation had a system of reciprocity involving effigies called manitohkan with food, tools and medicine for traveling. Leaving unneeded items at these can be perceived as cooperative actions; a reciprocal sharing of resources or caching of items. Mācistan is a novel MiniGrid environment built off the key-to-door task most common in temporal credit assignment reinforcement learning literature. However, abstracting to multi-agent dimension has led to interesting behaviour and the structural credit assignment problem.
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