Deep Reinforcement Learning (deep RL) has made several breakthroughs in recent years in applications ranging from complex control tasks in unmanned vehicles to game playing. Despite their success, deep RL still lacks several important capacities of human intelligence, such as transfer learning, abstraction and interpretability. Deep Symbolic Reinforcement Learning (DSRL) seeks to incorporate such capacities to deep Q-networks (DQN) by learning a relevant symbolic representation prior to using Q-learning. In this paper, we propose a novel extension of DSRL, which we call Symbolic Reinforcement Learning with Common Sense (SRL+CS), offering a better balance between generalization and specialization, inspired by principles of common sense when assigning rewards and aggregating Q-values. Experiments reported in this paper show that SRL+CS learns consistently faster than Q-learning and DSRL, achieving also a higher accuracy. In the hardest case, where agents were trained in a deterministic environment and tested in a random environment, SRL+CS achieves nearly 100% average accuracy compared to DSRL's 70% and DQN's 50% accuracy. To the best of our knowledge, this is the first case of near perfect zero-shot transfer learning using Reinforcement Learning.

本论文提出了一种名为 Symbolic Reinforcement Learning with Common Sense (SRL+CS) 的算法，它在奖励分配和 Q 值聚合时结合了常识原则，使得在转移学习和零-shot 转移学习等方面具有更好的泛化和特化表现。实验结果表明，SRL+CS 算法比 Q-learning 和 DSRL 算法更为快速且更准确，是近乎完美零-shot 转移学习在强化学习领域的首次尝试。

走向具备常识的符号强化学习