  Multiagent systems provide an ideal environment for the evaluation and
analysis of real-world problems using reinforcement learning algorithms. Most
traditional approaches to multiagent learning are affected by long training
periods as well as high computational complexity. NEAT (NeuroEvolution of
Augmenting Topologies) is a popular evolutionary strategy used to obtain the
best performing neural network architecture often used to tackle optimization
problems in the field of artificial intelligence. This paper utilizes the NEAT
algorithm to achieve competitive multiagent learning on a modified pong game
environment in an efficient manner. The competing agents abide by different
rules while having similar observation space parameters. The proposed algorithm
utilizes this property of the environment to define a singular
neuroevolutionary procedure that obtains the optimal policy for all the agents.
The compiled results indicate that the proposed implementation achieves ideal
behaviour in a very short training period when compared to existing multiagent
reinforcement learning models.
