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We apply our method to learning maximum entropy policies, resulting\ninto a new algorithm, called soft Q-learning, that expresses the optimal policy\nvia a Boltzmann distribution. We use the recently proposed amortized Stein\nvariational gradient descent to learn a stochastic sampling network that\napproximates samples from this distribution. The benefits of the proposed\nalgorithm include improved exploration and compositionality that allows\ntransferring skills between tasks, which we confirm in simulated experiments\nwith swimming and walking robots. We also draw a connection to actor-critic\nmethods, which can be viewed performing approximate inference on the\ncorresponding energy-based model.","url_abs":"http://arxiv.org/abs/1702.08165v2","url_pdf":"http://arxiv.org/pdf/1702.08165v2.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":"reinforcement-learning-with-deep-energy-based","repo_url":"https://github.com/haarnoja/softqlearning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"reinforcement-learning-with-deep-energy-based","repo_url":"https://github.com/mpatacchiola/gym-multigoal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"reinforcement-learning-with-deep-energy-based","repo_url":"https://github.com/shakedzy/warehouse","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"reinforcement-learning-with-deep-energy-based","repo_url":"https://github.com/opendilab/DI-engine/blob/main/ding/policy/sql.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"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":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1702.08165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.08165"}},"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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