Future-proofing Video Game Agents with Reinforced Learning and Unity ML-Agents

Sammanfattning: In later years, a number of simulation platforms has utilized video games as training grounds for designing and experimenting with different Machine Learning algorithms. One issue for many is that video games usually do not provide any source code. The Unity ML-Agents toolkit provides both example environments and state-of-the-art Machine Learning algorithms in an attempt solve this. This has sparked curiosity in a local game company which wished to investigate the incorporation of machine-learned agents into their game using the toolkit. As such, the goal was to produce high performing, integrable agents capable of completing locomotive tasks. A pilot study was conducted which contributed with insight in training functionality and aspect which were important to producing a robust behavior model. With the use of Proximal Policy Optimization and different training configurations several neural network models were produced and evaluated on existing and new data. Several of the produced models displayed promising results but did not achieve the defined success rate of 80%. With some additional testing it is believed that the desired result could be reached. Alternatively, different aspect of the toolkit like Soft Actor Critic and Curriculum Learning could be investigated.