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It is difficult to automatically discovering hierarchies in multi-agent reinforcement learning. We consider an immune clustering approach for automatically discovering hierarchies in option learning framework. The leading agent generates an undirected edge-weighted topological graph of the environment state transitions based on the environment information explored by all agents. An immune clustering...
An open problem in hierarchical reinforcement learning is how to automatically generate hierarchies, e.g. options. We consider an immune clustering approach for automatic construction of options in a dynamic environment. The learning agent generates an undirected edge-weighted topological graph of the environment state transitions online. An immune clustering algorithm is then used to partition the...
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