Application of Deep-RL with Sample-Efficient Method in Mini-games of StarCraft II

Recently, a key challenge of deep reinforcement learning (Deep-RL) is to handle a large amount of samples and learning time in domains with large state and action space. To remedy these problems, we focus on improving the sample efficiency of Deep-RL. We incorporate SIL into the state-of-the-art algorithm IMPALA in learning mini-games of StarCraft II, which has been a challenge to Deep-RL. Our results show that our agents achieve better performance with faster and more stable learning than those trained by the plain IMPALA on two mini-games of StarCraft II.