Multiagent-reinforcement-learning-algorithms-for-multiple-UAV-confrontation
This is the source code of "Efficient training techniques for multi-agent reinforcement learning in combatant tasks".
This is the source code of "Efficient training techniques for multi-agent reinforcement learning in combatant tasks", we construct a multi-agent confrontation environment originated from a combatant scenario of multiple unman aerial vehicles. To begin with, we consider to solve this confrontation problem with two types of MARL algorithms. One is extended from the classical deep Q-network for multi-agent settings (MADQN). The other one is extended from the state-of-art multi-agent reinforcement method, multi-agent deep deterministic policy gradient (MADDPG).
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# We never proxy the call.
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