Real-time PvP games often suffer from lag while waiting for responses from the server.We realized that in multiplayer games involving strategy, movement can be predicted with an LSTM neural network.
What it does
A neural network predicts the position of objects to counter lag. We demonstrated this concept by creating a web game that communicated with the server every 0.75 seconds. At the click of a button, the 0.75-second interval is filled with the predicted movement, and the lag vanishes.
How we built it
Challenges we ran into
- Sending the neural network to clients.
- Communication between different parts of the application while lag existed.
- Making the page responsive in a satisfactory manner (bootstrap's dynamic sizing was not to our satisfaction)
- We were planning to use Keras.js to train the network in python and send the trained model to the browsers, but we ran into trouble and had to switch libraries.
Accomplishments that we're proud of
- Finishing the Keras model
- The socket programming for real-time multiplayer interaction
- User interface design
What we learned
- More about machine learning
- Manual implementation of sockets
What's next for Lagacetamol
Attempt to apply a similar LSTM architecture to different, more complex real-time multiplayer games.