Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for dealing with the learning and control issues in cooperative multiagent systems with continuous states and actions, particularly for autonomous robotic formation systems. The parameters of fuzzy policy are finely tuned by the gradient multiagent reinforcement learning algorithm to improve the overall performance of an initial controller (policy). A leader-follor robotic system is chosen as a platform to benchmark the performance of the multiagent fuzzy policy reinforcement learning algorithm. Our simulation results demonstrate that the control performance can be improved in many aspects. This work also can be seen as a scaling up of currently popular multiagent reinforcement learning to the robotic domain with continuous state and action space as ll as high dimensionality.
['Erfu Yang', 'Dongbing Gu']
I am writing a research paper on a idea A Multiagent Fuzzy Policy Reinforcement Learning Algorithm with Application to Leader-Follower Robotic Systems
[{"from": "human", "value": "I am writing a research paper on a idea A Multiagent Fuzzy Policy Reinforcement Learning Algorithm with Application to Leader-Follower Robotic Systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for dealing with the learning and control issues in cooperative multiagent systems with continuous states and actions, particularly for autonomous robotic formation systems. The parameters of fuzzy policy are finely tuned by the gradient multiagent reinforcement learning algorithm to improve the overall performance of an initial controller (policy). A leader-follor robotic system is chosen as a platform to benchmark the performance of the multiagent fuzzy policy reinforcement learning algorithm. Our simulation results demonstrate that the control performance can be improved in many aspects. This work also can be seen as a scaling up of currently popular multiagent reinforcement learning to the robotic domain with continuous state and action space as ll as high dimensionality."}]