Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents.   are interested in using Q-learning to learn the coordinated actions of a group of cooperative agents, using a sparse representation of the joint state-action space of the agents.  first examine a compact representation in which the agents need to explicitly coordinate their actions only in a predefined set of states. Next,  use a coordination-graph approach in which  represent the Q-values by value rules that specify the coordination dependencies of the agents at particular states.  show how Q-learning can be efficiently applied to learn a coordinated policy for the agents in the above framework.  demonstrate the proposed method on the predator-prey domain, and  compare it with other related multiagent Q-learning methods.
['Jelle R. Kok', 'Nikos A. Vlassis']
I am writing a research paper on a idea Sparse cooperative Q-learning
[{"from": "human", "value": "I am writing a research paper on a idea Sparse cooperative Q-learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents.   are interested in using Q-learning to learn the coordinated actions of a group of cooperative agents, using a sparse representation of the joint state-action space of the agents.  first examine a compact representation in which the agents need to explicitly coordinate their actions only in a predefined set of states. Next,  use a coordination-graph approach in which  represent the Q-values by value rules that specify the coordination dependencies of the agents at particular states.  show how Q-learning can be efficiently applied to learn a coordinated policy for the agents in the above framework.  demonstrate the proposed method on the predator-prey domain, and  compare it with other related multiagent Q-learning methods."}]