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@article{153188,
        author = {Kamalakannan and Vignesh and Monisha},
        title = {Multi user deep reinforcement learning using deep q network},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {6},
        pages = {627-630},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153188},
        abstract = {The problem in the DSA dynamic spectrum is the path of the distance between the nodes into the environment. The maximization in multichannel wireless networks. Where there is the problem which is it to take the shortest path by the node in the environment by the agent. This will make the message flow between the distance of the node. By this within it the certain attempt probability. From after into it time slot, Where the user of each node is delivered the packet successfully into the DSA. where into the distributed manner without online coordination or into it. The optimal rectification for this is make high cost due to this observe of the agent that states in the environment.  So, for this to tackle this problem, so for that we developed a DSA algorithm which is shows the specific time slot into the environment by this we access the shortest action path and pass  coordinate the message in o the environment. Which shows the principle for the implementation of the algorithm.},
        keywords = {Wireless networks, multi-user DSA, Agent, Environment, states and action, multi-agent learning, deep reinforcement learning.},
        month = {},
        }