Projects:2017s2-220 Alternative Approaches to AI for the Soccer Table

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The project aims at the development of an AI agent that can learn to play the table-top game Foosball in a simulated environment. Our objective is for the agent to be able to, on average, play better than a consistent, randomly moving opponent. Another objective is for the software integrating the simulation and agent training to be a useable training testbed. This project is a case-study application of the emerging field of Reinforced Deep Learning and attempts to replicate a Dueling Double Deep Q Network model by DeepMind.

The decision-making, deep-learning agent perceives its environment and takes actions that maximise its chance of success in the game. The agent is trained using Reinforcement Learning in a software simulation of a Foosball table. The simulated environment to which the agent interacts was implemented from a game’s source code.

The software package TensorFlow is used in the language Python, and the learning process is executed on high-performance computing system. The learning process allows the agent to develop its Foosball-playing ability.

When trained for 750,000 game frames, with a simplified game test-case, the agent clearly learns to intercept the ball. However, with a full game test-case, the agent does not show any obvious learnt intelligent behaviour. Likely due to a limitation in memory allocation to training and efficiency, the agent was not able to replicate the previous success of the model by DeepMind.

Project Team


Daniel Calandro

Liang Xu


Dr Braden Phillips

Dr Hong Gunn Chew