RAIL: Risk-Averse Imitation Learning

author-image

By

Imitation learning algorithms learn viable policies by imitating an expert’s behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies when the expert’s behavior is available as a fixed set of trajectories...

Authors

Director, Parallel Computing Lab, Intel Labs India

View authors bio

Show more Show less

Related Content

We present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments....

View publication

We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support...

View publication

Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using...

View publication

We present an approach to sensorimotor control in immersive environments. Our approach utilizes a high-dimensional sensory stream and a lower-dimensional...

View publication

Show more Show less

Stay Connected

Keep tabs on all the latest news with our monthly newsletter.

Subscribe

@intelai

@intelairesearch

@intelai

intel-ai

intelai

Intel Newsroom

Intel® AI Builders

intel ai events

Authors
Related Content

<script>var isTargetEnabled =false;</script>

<script type="text/javascript">
    var cq_tms = {
        wa_emtcontenttype: "emtcontenttype:donotuse/webpage/landingpage",
        wa_emtsubject: "emtsubject:itinformationtechnology/aiartificialintelligence",
        
        wa_industry_type: "",
        wa_partner_prog: "",
        wa_subject: "",
        wa_biz_size: "",
        wa_emt_org: "",
        wa_emt_intel: "",
        wa_partner_type: "",
        wa_programidentifier: "none",
        wa_location: "us",
        wa_language: "en",
        wa_english_title: "RAIL: Risk\u002DAverse Imitation Learning",
        wa_content_type: "landingpage",
        wa_intel_platform: "",
        wa_applications: "",
        wa_local: "en_US",
        wa_intel_technology: "",
        wa_system_type: "",
        wa_product_name: "",
        wa_audience: "emtaudience:business/btssbusinesstechnologysolutionspecialist/developer/artificialintelligenceaiapplicationdeveloper",
        wa_life_cycle: "",
        wa_cq_url: "/content/www/us/en/artificial-intelligence/researches/rail-risk-averse-imitation-learning.html",
        wa_page_type_micro: "mf-article-long",
        wa_env: "prd",
        wa_cq_pub_env: "publish",
        wa_transl_status: "",
        wa_product_id: "",
        wa_software: "",
        wa_reference_design: "",
        wa_codename: "",
        wa_created_date: "1585760855718",
        wa_modified_date: "1668681078946",
        wa_off_time: "1674028800000",
        wa_ownedby: "",
        wa_managedby: "",
        wa_idz_nid : "",
        wa_event_type: "",
        wa_idz_custom_tags: "",
        wa_programming_language: "",
        wa_skill_level: "",
        wa_solution: "",
        wa_product_formFactor: "",
        wa_operating_system: "",
        wa_marketing_products: "",
        wa_ssg_third_party_prod_sys_sol: "",
        wa_upeNamespace: "",
        wa_doc_id:"",
        wa_doc_version:"",
        wa_doc_publishdate:"",
        
        wa_secondary_content_type: ""
        
        
        
    };

cq_tms.wa_tms_sync_load = "deferred";
    
    
</script>

<script type="text/javascript">
    var chatContextObj = {};
    chatContextObj["locale"] = 'en-US';
    chatContextObj["app"] = 'Sales';
    chatContextObj["supportProductMap"] = 'NA';
</script>
<script src="/etc.clientlibs/settings/wcm/designs/ver/221120/intel/clientlibs/detect-idsa.min.js" defer></script>

<script type="text/javascript" src="/content/dam/www/global/wap/tms.sync.js" async></script>

<link rel="stylesheet" href="/etc.clientlibs/settings/wcm/designs/ver/221120/intel/clientlibs/pages/commons-page.min.css" type="text/css"><script src="/etc.clientlibs/settings/wcm/designs/ver/221120/intel/clientlibs/pages/commons-page.min.js" defer></script>

<link rel="preload" href="/etc.clientlibs/settings/wcm/designs/ver/221120/intel/clientlibs/pages/contact-us.min.css" as="style"><link rel="stylesheet" href="/etc.clientlibs/settings/wcm/designs/ver/221120/intel/clientlibs/pages/contact-us.min.css" type="text/css">

<script>!function(){var e=setInterval(function(){"undefined"!=typeof $CQ&&($CQ(function(){CQ_Analytics.SegmentMgr.loadSegments("/etc/segmentation"),CQ_Analytics.ClientContextUtils.init("/etc/clientcontext/intel",window.location.pathname.substr(0,window.location.pathname.indexOf(".")))}),clearInterval(e))},100)}();</script>

<link rel="preload" as="style" href="/etc.clientlibs/settings/wcm/designs/intel/us/en/css/resources/css/intel.rwd.override.css"/>
<link rel="stylesheet" type="text/css" href="/etc.clientlibs/settings/wcm/designs/intel/us/en/css/resources/css/intel.rwd.override.css"/>