Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Advances in deep reinforcement learning have allod autonomous agents to perform ll on Atari games, often outperforming humans, using only raw pixels to make their decisions. Hover, most of these games take place in 2D environments that are fully observable to the agent. ,  present the first architecture to tackle 3D environments in first-person shooter games, that involve partially observable states. Typically, deep reinforcement learning methods only utilize visual input for training.  present a method to augment these models to exploit game feature information such as the presence of enemies or items, during the training phase. Our model is trained to simultaneously learn these features along with minimizing a Q-learning objective, which is shown to dramatically improve the training speed and performance of our agent. Our architecture is also modularized to allow different models to be independently trained for different phases of the game.  show that the proposed architecture substantially outperforms built-in AI agents of the game as ll as humans in deathmatch scenarios.
['Guillaume Lample', 'Devendra Singh Chaplot']
I am writing a research paper on a idea Playing FPS Games with Deep Reinforcement Learning
[{"from": "human", "value": "I am writing a research paper on a idea Playing FPS Games with Deep Reinforcement Learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Advances in deep reinforcement learning have allod autonomous agents to perform ll on Atari games, often outperforming humans, using only raw pixels to make their decisions. Hover, most of these games take place in 2D environments that are fully observable to the agent. ,  present the first architecture to tackle 3D environments in first-person shooter games, that involve partially observable states. Typically, deep reinforcement learning methods only utilize visual input for training.  present a method to augment these models to exploit game feature information such as the presence of enemies or items, during the training phase. Our model is trained to simultaneously learn these features along with minimizing a Q-learning objective, which is shown to dramatically improve the training speed and performance of our agent. Our architecture is also modularized to allow different models to be independently trained for different phases of the game.  show that the proposed architecture substantially outperforms built-in AI agents of the game as ll as humans in deathmatch scenarios."}]