An Efficient Application of Neuroevolution for Competitive Multiagent Learning
This work addresses efficiency issues in multiagent learning for competitive environments, but it is incremental as it adapts an existing method to a specific domain.
The paper tackles the problem of long training times and high computational complexity in multiagent reinforcement learning by applying the NEAT algorithm to a modified pong game environment, achieving ideal behavior in a very short training period compared to existing models.
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.