Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Computer generated forces (CGFs) inhabiting air combat training simulations must show realistic and adaptive behavior to effectively perform their roles as allies and adversaries. In earlier work, behavior for these CGFs was successfully generated using reinforcement learning. Hover, due to missile hits being subject to chance (a.k.a. The probability of-kill), the CGFs have in certain cases been improperly rewarded and punished.  surmise that taking this probability of-kill into account in the reward function will improve performance. To remedy the false rewards and punishments, a new reward function is proposed that rewards agents based on the expected outcome of their actions. Tests show that the use of this function significantly increases the performance of the CGFs in various scenarios, compared to the previous reward function and a naive baseline. Based on the results, the new reward function allows the CGFs to generate more intelligent behavior, which enables better training simulations.
['Armon Toubman', 'J.J.M. Roessingh', 'Pieter Spronck', 'Aske Plaat', 'H. Jaap van den Herik']
I am writing a research paper on a idea Rewarding Air Combat Behavior in Training Simulations
[{"from": "human", "value": "I am writing a research paper on a idea Rewarding Air Combat Behavior in Training Simulations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Computer generated forces (CGFs) inhabiting air combat training simulations must show realistic and adaptive behavior to effectively perform their roles as allies and adversaries. In earlier work, behavior for these CGFs was successfully generated using reinforcement learning. Hover, due to missile hits being subject to chance (a.k.a. The probability of-kill), the CGFs have in certain cases been improperly rewarded and punished.  surmise that taking this probability of-kill into account in the reward function will improve performance. To remedy the false rewards and punishments, a new reward function is proposed that rewards agents based on the expected outcome of their actions. Tests show that the use of this function significantly increases the performance of the CGFs in various scenarios, compared to the previous reward function and a naive baseline. Based on the results, the new reward function allows the CGFs to generate more intelligent behavior, which enables better training simulations."}]