Aerospace battle scenarios represent a challenging modeling effort, often requiring large, continuous, and simultaneous state and/ or action spaces with imperfect information. We model a battle as a Multi-Stage Markov Stochastic Game (MSMSG) and facilitate agent decision making using a Double Deep Q-Network (DDQN) paradigm with Minimax Q-Learning. We demonstrate our model performance in contrast with a DDQN agent trained using a traditional Q-learning algorithm in a 1D dynamic battle environment. Preliminary findings suggest that the DDQN + Minimax-Q agent is more robust to parameter tuning and can learn true optimal mixed strategies compared to its traditional Q-learning counterpart.


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    Title :

    Agent Decision Processes Using Double Deep Q-Networks + Minimax Q- Learning


    Contributors:


    Publication date :

    2021-03-06


    Size :

    7037750 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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