Deep vs. Deep Bayesian: Reinforcement Learning on a Multi-Robot Competitive Experiment

Deep Reinforcement Learning (RL) experiments are commonly performed in simulated environment, due to the tremendous training sample demand from deep neural networks. However, model-based Deep Bayesian RL, such as Deep PILCO, allows a robot to learn good policies within few trials in the real world. Although Deep PILCO has been applied on many single-robot tasks, in here we propose, for the first time, an application of Deep PILCO on a multi-robot confrontation game, and compare the algorithm with a model-free Deep RL algorithm, Deep Q-Learning. Our experiments show that Deep PILCO significantly outperforms Deep Q-Learning in learning efficiency and scalability. We conclude that sample-efficient Deep Bayesian learning algorithms have great prospects on competitive games where the agent aims to win the opponents in the real world, as opposed to simulated applications.