{"id":659097,"date":"2020-05-14T11:11:45","date_gmt":"2020-05-14T18:11:45","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=659097"},"modified":"2025-09-02T07:31:52","modified_gmt":"2025-09-02T14:31:52","slug":"a-reduction-from-reinforcement-learning-to-no-regret-online-learning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-reduction-from-reinforcement-learning-to-no-regret-online-learning\/","title":{"rendered":"A Reduction from Reinforcement Learning to No-Regret Online Learning"},"content":{"rendered":"

We present a reduction from reinforcement learning (RL) to no-regret online learning based on the saddle-point formulation of RL, by which “any” online algorithm with sublinear regret can generate policies with provable performance guarantees. This new perspective decouples the RL problem into two parts: regret minimization and function approximation. The first part admits a standard online-learning analysis, and the second part can be quantified independently of the learning algorithm. Therefore, the proposed reduction can be used as a tool to systematically design new RL algorithms. We demonstrate this idea by devising a simple RL algorithm based on mirror descent and the generative-model oracle. For any\u00a0\u03b3<\/span><\/span><\/span><\/span>-discounted tabular RL problem, with probability at least\u00a01<\/span>\u2212<\/span>\u03b4<\/span><\/span><\/span><\/span>, it learns an\u00a0\u03f5<\/span><\/span><\/span><\/span>-optimal policy using at most\u00a0O<\/span><\/span><\/span><\/span>(<\/span>|<\/span><\/span><\/span>S<\/span><\/span><\/span>|<\/span><\/span><\/span>|<\/span><\/span><\/span>A<\/span><\/span><\/span>|<\/span><\/span><\/span>log<\/span><\/span>(<\/span>1\/<\/span>\u03b4<\/span><\/span>) \/ <\/span><\/span>(<\/span>1<\/span>\u2212<\/span>\u03b3<\/span>)^<\/span>4<\/span><\/span>\u03f5^<\/span>2<\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span><\/span>\u00a0samples. Furthermore, this algorithm admits a direct extension to linearly parameterized function approximators for large-scale applications, with computation and sample complexities independent of\u00a0|<\/span><\/span><\/span>S<\/span><\/span><\/span>|<\/span><\/span><\/span><\/span><\/span><\/span>,|<\/span><\/span><\/span>A<\/span><\/span><\/span>|<\/span><\/span><\/span><\/span><\/span><\/span>, though at the cost of potential approximation bias.<\/p>\n","protected":false},"excerpt":{"rendered":"

We present a reduction from reinforcement learning (RL) to no-regret online learning based on the saddle-point formulation of RL, by which “any” online algorithm with sublinear regret can generate policies with provable performance guarantees. This new perspective decouples the RL problem into two parts: regret minimization and function approximation. The first part admits a standard […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"International Conference on Artificial Intelligence and 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