Deep Reinforcement Learning with Successive Over-Relaxation and its Application in Autoscaling Cloud Resources

We present a new deep reinforcement learning algorithm using the technique of successive over-relaxation (SOR) in Deep Q-networks (DQNs). The new algorithm, named SOR-DQN, uses modified targets in the DQN framework with the aim of accelerating training. This work is motivated by the problem of auto-scaling resources for cloud applications, for which existing algorithms suffer from issues such as slow convergence, poor performance during the training phase and non-scalability. For the above problem, SOR-DQN achieves significant improvements over DQN on both synthetic and real datasets. We also study the generalization ability of the algorithm to multiple tasks by using it to train agents playing Atari video games.