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UID:MEC-fca0789e7891cbc0583298a238316122@yorku.ca
DTSTART:20260122T160000Z
DTEND:20260122T173000Z
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CREATED:20260107
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SUMMARY:CAIS Seminar: Sharan Vaswani
DESCRIPTION:Title: A Systematic Framework for Designing Policy Gradient Methods for Reinforcement Learning\n\n\n/* "function"==typeof InitializeEditor,callIfLoaded:function(o){return!(!gform.domLoaded||!gform.scriptsLoaded||!gform.themeScriptsLoaded&&!gform.isFormEditor()||(gform.isFormEditor()&&console.warn("The use of gform.initializeOnLoaded() is deprecated in the form editor context and will be removed in Gravity Forms 3.1."),o(),0))},initializeOnLoaded:function(o){gform.callIfLoaded(o)||(document.addEventListener("gform_main_scripts_loaded",()=>{gform.scriptsLoaded=!0,gform.callIfLoaded(o)}),document.addEventListener("gform/theme/scripts_loaded",()=>{gform.themeScriptsLoaded=!0,gform.callIfLoaded(o)}),window.addEventListener("DOMContentLoaded",()=>{gform.domLoaded=!0,gform.callIfLoaded(o)}))},hooks:{action:{},filter:{}},addAction:function(o,r,e,t){gform.addHook("action",o,r,e,t)},addFilter:function(o,r,e,t){gform.addHook("filter",o,r,e,t)},doAction:function(o){gform.doHook("action",o,arguments)},applyFilters:function(o){return gform.doHook("filter",o,arguments)},removeAction:function(o,r){gform.removeHook("action",o,r)},removeFilter:function(o,r,e){gform.removeHook("filter",o,r,e)},addHook:function(o,r,e,t,n){null==gform.hooks[o][r]&&(gform.hooks[o][r]=[]);var d=gform.hooks[o][r];null==n&&(n=r+"_"+d.length),gform.hooks[o][r].push({tag:n,callable:e,priority:t=null==t?10:t})},doHook:function(r,o,e){var t;if(e=Array.prototype.slice.call(e,1),null!=gform.hooks[r][o]&&((o=gform.hooks[r][o]).sort(function(o,r){return o.priority-r.priority}),o.forEach(function(o){"function"!=typeof(t=o.callable)&&(t=window[t]),"action"==r?t.apply(null,e):e[0]=t.apply(null,e)})),"filter"==r)return e[0]},removeHook:function(o,r,t,n){var e;null!=gform.hooks[o][r]&&(e=(e=gform.hooks[o][r]).filter(function(o,r,e){return!!(null!=n&&n!=o.tag||null!=t&&t!=o.priority)}),gform.hooks[o][r]=e)}});\n/* ]]> */\n\n\n                \n                        \n                            Please register for the seminar. Registration is required.\n                        \n                        First Name(Required)Last Name(Required)Email(Required)FacultyArts, Media, Performance & DesignFaculty of EducationFaculty of Environmental & Urban ChangeGlendon CollegeFaculty of Graduate StudiesFaculty of HealthLassonde School of EngineeringFaculty of Liberal Arts & Professional Studies (LA&PS)Osgoode Hall Law SchoolSchulich School of BusinessFaculty of ScienceN/A (select this if you are staff or external)AffiliationYork FacultyYork PostdocYork Graduate StudentYork Undergraduate StudentYork StaffGuest (outside York)My CAIS affiliation(Required)\n								\n								I am CAIS Faculty\n							\n								\n								I am a CAIS Trainee (postdoc, grad, undergrad)\n							\n								\n								I am not a CAIS Member\n							\n								\n								I am not a CAIS Member but would like to become a member\n							\n								\n								N/A or Other\n							If you wish to become a member, please complete the membership form at https://machform.osgoode.yorku.ca/machform/view.php?id=226829 I confirm my registration for the seminar.(Required)\n								\n								YES\n							\n          \n            \n            \n            \n            \n            \n            \n            \n            \n            \n            \n            \n            \n            \n        \n                        \n                        \n/* = 0;if(!is_postback){return;}var form_content = jQuery(this).contents().find('#gform_wrapper_7');var is_confirmation = jQuery(this).contents().find('#gform_confirmation_wrapper_7').length > 0;var is_redirect = contents.indexOf('gformRedirect(){') >= 0;var is_form = form_content.length > 0 && ! 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RL has found applications in medicine, industrial control, robotics, and, more recently, for reasoning with large language models. Policy gradient (PG) methods are a widely used approach in RL, providing a direct way to optimize decision-making performance by using gradient ascent on the expected cumulative reward. Common PG algorithms improve the policy by iteratively optimizing surrogate objectives that approximate the true objective. While effective in practice, these surrogates are often designed in an ad hoc manner.\nIn this talk, we present a systematic framework for designing surrogate objectives for PG methods. The framework encompasses existing algorithms, explains common implementation heuristics, and provides a principled way to develop new methods. As an example, we introduce Softmax Policy Mirror Ascent (SPMA), a simple algorithm that is easy to implement, supports modern non-linear function approximation, and admits theoretical convergence guarantees in simplified settings. We demonstrate its empirical effectiveness on standard benchmarks, where it achieves performance comparable to or better than widely used state-of-the-art methods.\n\nBio: Sharan Vaswani is an Assistant Professor in the School of Computing Science at Simon Fraser University. Sharan obtained his PhD at the University of British Columbia and was a postdoctoral researcher at Mila - Quebec AI Institute and the University of Alberta. His research interests include designing better algorithms for sequential decision-making under uncertainty and optimization for machine learning.\n
URL:https://www.yorku.ca/research/cais/calendar/cais-seminar-sharan-vaswani/
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