{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-symbolic-reinforcement-learning-with","title":"Towards Symbolic Reinforcement Learning with Common Sense","arxiv_id":"1804.08597","date":"2018-04-23","proceeding":null,"authors":["Artur d'Avila Garcez","Aimore Resende Riquetti Dutra","Eduardo Alonso"],"abstract":"Deep Reinforcement Learning (deep RL) has made several breakthroughs in\nrecent years in applications ranging from complex control tasks in unmanned\nvehicles to game playing. Despite their success, deep RL still lacks several\nimportant capacities of human intelligence, such as transfer learning,\nabstraction and interpretability. Deep Symbolic Reinforcement Learning (DSRL)\nseeks to incorporate such capacities to deep Q-networks (DQN) by learning a\nrelevant symbolic representation prior to using Q-learning. In this paper, we\npropose a novel extension of DSRL, which we call Symbolic Reinforcement\nLearning with Common Sense (SRL+CS), offering a better balance between\ngeneralization and specialization, inspired by principles of common sense when\nassigning rewards and aggregating Q-values. Experiments reported in this paper\nshow that SRL+CS learns consistently faster than Q-learning and DSRL, achieving\nalso a higher accuracy. In the hardest case, where agents were trained in a\ndeterministic environment and tested in a random environment, SRL+CS achieves\nnearly 100% average accuracy compared to DSRL's 70% and DQN's 50% accuracy. To\nthe best of our knowledge, this is the first case of near perfect zero-shot\ntransfer learning using Reinforcement Learning.","url_abs":"http://arxiv.org/abs/1804.08597v1","url_pdf":"http://arxiv.org/pdf/1804.08597v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-symbolic-reinforcement-learning-with","repo_url":"https://github.com/AimoreRRD/AIMORE_GAME","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-2.0"}}],"tasks":[{"task_slug":"common-sense-reasoning","task_name":"Common Sense Reasoning"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"q-learning","method_name":"Q-Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.08597","atlas_url":"https://app.syntology.ai/?focus=1804.08597","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}