Over the past couple of weeks, progress on this project has been slow but meaningful. Matthew and I have decided to do a temporary re-scoping of the project. Instead of focusing on a 3-D game, we are going to move to a simpler 2-D game. The game is the only real change we have made though, as we still want it to infer a game state from the visual buffer.
The game we have chosen is the 2-D pixel fighter Rivals of Aether.
The game we have chosen is the 2-D pixel fighter Rivals of Aether.
We chose this game primarily because of how it outputs replay data. Rivals of Aether stores its replay data as plain-text. More specifically it stores input data as 'tuples' of (InputFrame, Input) for example
Currently, the plan Matthew and I have agreed upon is to work primarily with Rivals of Aether. If our implementation works well and we feel as if we can safely scope-up, we will move from Rivals to Quake.
'5134y' is saying press the 'Y' button on frame 5134. Using this we can gather more data for our RivalsAgent to learn from. Currently, the plan Matthew and I have agreed upon is to work primarily with Rivals of Aether. If our implementation works well and we feel as if we can safely scope-up, we will move from Rivals to Quake.
As I promised in Post 2, I have also began looking into data flow in Neural Networks. From what research I have done and considering the scope of our project, I have found and begun research on two classes of neural networks. The neural networks are Feedforward Neural Networks and Recurrent Neural Networks.
Feedforward Neural Networks are the first and simplest type of neural network. Connections between units do not form a cycle. Information in A feed forward network moves in one direction. This leads to this type of network to be used for the "supervised learning of binary classifiers" meaning that an input either belongs to some class or does not. For example, a network that tries to find correlations between two related data sets could use feed-forward.
Recurrent Neural Networks on the other hand do have cycles. These cycles allow the network to use previously learned data to learn more about both the same data and similar data. Many recognition algorithims use these types of networks. For example, a Handwriting Recognition tool would need to learn about how the user writes letters, and then can use that data to learn how the user writes words and so on.
Combining feed forward and recurrent networks in modular systems has been done many times before, regardless our project will most likely combine these networks to reach our end goal.
Feedforward Neural Networks are the first and simplest type of neural network. Connections between units do not form a cycle. Information in A feed forward network moves in one direction. This leads to this type of network to be used for the "supervised learning of binary classifiers" meaning that an input either belongs to some class or does not. For example, a network that tries to find correlations between two related data sets could use feed-forward.
Recurrent Neural Networks on the other hand do have cycles. These cycles allow the network to use previously learned data to learn more about both the same data and similar data. Many recognition algorithims use these types of networks. For example, a Handwriting Recognition tool would need to learn about how the user writes letters, and then can use that data to learn how the user writes words and so on.
Combining feed forward and recurrent networks in modular systems has been done many times before, regardless our project will most likely combine these networks to reach our end goal.
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