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SAC-X enables learning of complex\nbehaviors - from scratch - in the presence of multiple sparse reward signals.\nTo this end, the agent is equipped with a set of general auxiliary tasks, that\nit attempts to learn simultaneously via off-policy RL. The key idea behind our\nmethod is that active (learned) scheduling and execution of auxiliary policies\nallows the agent to efficiently explore its environment - enabling it to excel\nat sparse reward RL. Our experiments in several challenging robotic\nmanipulation settings demonstrate the power of our approach.","url_abs":"http://arxiv.org/abs/1802.10567v1","url_pdf":"http://arxiv.org/pdf/1802.10567v1.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":"learning-by-playing-solving-sparse-reward","repo_url":"https://github.com/hu-po/pySACQ","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"learning-by-playing-solving-sparse-reward","repo_url":"https://github.com/utiasSTARS/vpace","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"scheduling","task_name":"Scheduling"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.10567","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10567"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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