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Lang."],"published-print":{"date-parts":[[2024,6,20]]},"abstract":"<jats:p>Deep reinforcement learning (RL) has led to encouraging successes in numerous challenging robotics applications. However, the lack of inductive biases to support logic deduction and generalization in the representation of a deep RL model causes it less effective in exploring complex long-horizon robot-control tasks with sparse reward signals. Existing program synthesis algorithms for RL problems inherit the same limitation, as they either adapt conventional RL algorithms to guide program search or synthesize robot-control programs to imitate an RL model. We propose ReGuS, a reward-guided synthesis paradigm, to unlock the potential of program synthesis to overcome the exploration challenges. We develop a novel hierarchical synthesis algorithm with decomposed search space for loops, on-demand synthesis of conditional statements, and curriculum synthesis for procedure calls, to effectively compress the exploration space for long-horizon, multi-stage, and procedural robot-control tasks that are difficult to address by conventional RL techniques. Experiment results demonstrate that ReGuS significantly outperforms state-of-the-art RL algorithms and standard program synthesis baselines on challenging robot tasks including autonomous driving, locomotion control, and object manipulation.<\/jats:p>\n          <jats:p>\n            CCS Concepts: \u2022\n            <jats:bold>Software and its engineering \u2192 Automatic programming.<\/jats:bold>\n          <\/jats:p>","DOI":"10.1145\/3656447","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T16:27:20Z","timestamp":1718900840000},"page":"1730-1754","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Reward-Guided Synthesis of Intelligent Agents with Control Structures"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7994-915X","authenticated-orcid":false,"given":"Guofeng","family":"Cui","sequence":"first","affiliation":[{"name":"Rutgers University, New Brunswick, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4317-9758","authenticated-orcid":false,"given":"Yuning","family":"Wang","sequence":"additional","affiliation":[{"name":"Rutgers University, New Brunswick, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2271-6443","authenticated-orcid":false,"given":"Wenjie","family":"Qiu","sequence":"additional","affiliation":[{"name":"Rutgers University, New Brunswick, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9606-150X","authenticated-orcid":false,"given":"He","family":"Zhu","sequence":"additional","affiliation":[{"name":"Rutgers University, New Brunswick, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"e_1_3_1_2_1","article-title":"State Abstraction for Programmable Reinforcement Learning Agents","author":"Andre David","year":"2002","unstructured":"David Andre and Stuart J. 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USA."},{"key":"e_1_3_1_40_1","article-title":"Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning.","author":"Chan Harris","year":"2020","unstructured":"Silviu Pitis, Harris Chan, Stephen Zhao, Bradly C . Stadie, and Jimmy Ba. 2020. Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020.","journal-title":"In Proceedings of the 37th International Conference on Machine Learning, ICML 2020."},{"key":"e_1_3_1_41_1","article-title":"Program synthesis from polymorphic refinement types","author":"Polikarpova Nadia","year":"2016","unstructured":"Nadia Polikarpova, Ivan Kuraj, and Armando Solar-Lezama. 2016. Program synthesis from polymorphic refinement types. In Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2016.","journal-title":"In Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2016"},{"key":"e_1_3_1_42_1","article-title":"Skew-Fit: State-Covering Self-Supervised Reinforcement Learning","author":"Pong Vitchyr","year":"2020","unstructured":"Vitchyr Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine. 2020. Skew-Fit: State-Covering Self-Supervised Reinforcement Learning. In Proceedings of the 37th International Conference on MachineLearning, ICML 2020.","journal-title":"In Proceedings of the 37th International Conference on MachineLearning, ICML 2020"},{"key":"e_1_3_1_43_1","unstructured":"Wenjie Qiu and He Zhu. 2022. Programmatic Reinforcement Learning without Oracles. In 10th International Conference on Learning Representations ICLR 2022."},{"key":"e_1_3_1_44_1","doi-asserted-by":"crossref","unstructured":"Joseph Redmon Santosh Kumar Divvala Ross B Girshick and Ali Farhadi. 2016. You Only Look Once: Unified Real-Time Object Detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2016. 779-788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_1_45_1","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal policy optimization algorithms. preprint arXiv:1707.06347 (2017)."},{"key":"e_1_3_1_46_1","doi-asserted-by":"crossref","unstructured":"Tom Silver Kelsey R. Allen Alex K. Lew Leslie Pack Kaelbling and Josh Tenenbaum. 2020. Few-Shot Bayesian Imitation Learning with Logical Program Policies. In The Thirty-Fourth AAAI Conference on Artificial Intelligence AAAI 2020.","DOI":"10.1609\/aaai.v34i06.6587"},{"key":"e_1_3_1_47_1","doi-asserted-by":"crossref","unstructured":"Tom Silver Rohan Chitnis Joshua B .Tenenbaum Leslie Pack Kaelbling and Tomas Lozano-Perez. 2021. Learning Symbolic Operators for Task and Motion Planning. In IEEE\/RSJ International Conference on Intelligent Robots and Systems IROS 2021.","DOI":"10.1109\/IROS51168.2021.9635941"},{"key":"e_1_3_1_48_1","doi-asserted-by":"crossref","unstructured":"Siddharth Srivastava Neil Immerman and Shlomo Zilberstein. 2011. A new representation and associated algorithms for generalized planning. Artif. Intell. (2011).","DOI":"10.1016\/j.artint.2010.10.006"},{"key":"e_1_3_1_49_1","doi-asserted-by":"crossref","unstructured":"Emanuel Todorov Tom Erez and Yuval Tassa. 2012. Mujoco: A physics engine for model-based control. 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