CamerAI: Chase Camera in a Dense Environment using a Proximal Policy Optimization-trained Neural Network
James Rucks, Nikolaos Katzakis · 2021 IEEE Conference on Games (CoG) · 2021
CamerAI is an autonomous chase camera that uses a PPO-trained neural network. Our results suggest that a simple, fully connected network with only two hidden layers of 128 neurons can perform basic chase camera functionality. We contribute a set of inputs and outputs with their respective coordinate spaces, as well as a custom reward function that can be used to train the network with reinforcement learning. Our findings highlight the importance of correct coordinate spaces, the need for a continuous reward function, normalization as well as timely resets during training to allow the network to explore its environment. We additionally present an evaluation of the output of CamerAI during 10 hours of chasing a bot that is randomly exploring a commercial video game map in which CamerAI was able to keep the player visible 96 % of the time.