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Agent Probing Interaction Policies
[article]
2019
arXiv
pre-print
Reinforcement learning in a multi agent system is difficult because these systems are inherently non-stationary in nature. In such a case, identifying the type of the opposite agent is crucial and can help us address this non-stationary environment. We have investigated if we can employ some probing policies which help us better identify the type of the other agent in the environment. We've made a simplifying assumption that the other agent has a stationary policy that our probing policy is
arXiv:1911.09535v3
fatcat:g3c3chnofffbngvdh4u6kt3odm