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{"body":"Explore This Park Learn About the Park History \u0026 Culture History \u0026 Culture History of the Baltimore-Washington ParkwayRunning between the eastern boundary of the District of Columbia and Baltimore, Maryland, the Baltimore-Washington Parkway is a 29-mile route that opened for traffic in October 1954. It resulted from the combined efforts of federal and state governments. The cooperation linked the two metropolitan areas along the \"fall line\" where the Atlantic coatal plain meets the Piedmont region.Initially, the parkway crossed undeveloped land. Since then, considerable suburban growth, stimulated in part by the roadway, has occured on either end.At present, the National Park Service manages the nineteen mile section from Fort Meade ( Route 32) to the district boundary. Gladys Noon Spellman Library of Congress Gladys Noon Spellman (March 1, 1918—June 19, 1988), a Democrat, was a U.S. Congresswoman who represented the 5th congressional district of Maryland from January 3, 1975 to January 3, 1981. On October 13, 1980, Spellman suffered an incapacitating heart attack, rendering her comatose for the final years of her life. Her seat was declared vacant until Steny Hoyer won it in special election. The Baltimore-Washington Parkway, a scenic north-south highway in Maryland, is dedicated to Spellman. Before being elected to public office, Gladys Spellman was an educator in the Prince George\u0027s County public school system, president of the Prince George\u0027s County Council of PTA\u0027s, as well as chairwoman of the National Mental Health Study Center. In 1962, she became the first woman elected to the Board of Commissioners of Prince George\u0027s County. She later served as chairperson of that body, and as a member of the Prince George\u0027s County Council. In 1972 she was awarded the highest honor that could be bestowed by county officials nationwide when she became the first woman elected president of the National Association of Counties.","url":"https://www.nps.gov/bawa/learn/historyculture/index.htm","title":"Baltimore-Washington Parkway: History \u0026 Culture"} |
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"body": "```\nCorrupt JSON data (indicates timeout or crash): \n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__32k_2_0.counters.new: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__8_2_0.opt.old: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__0_2_0.opt.old: 4\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__64_2_0.opt.old: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__0_2_0.counters.old: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__512_2_0.counters.new: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__512_2_0.counters.new: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__1_2_0.opt.new: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__512_2_0.opt.old: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__1_2_0.counters.new: 4\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__32k_2_0.opt.new: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__8_2_0.counters.old: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__1_2_0.opt.old: 5\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__4k_2_0.counters.old: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__8_2_0.counters.old: 6\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__4k_2_0.opt.new: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__4k_2_0.opt.new: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__4k_2_0.counters.new: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__8_2_0.opt.new: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__64_2_0.counters.new: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__512_2_0.opt.old: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__1_2_0.opt.new: 4\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__0_2_0.counters.old: 4\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__64_2_0.opt.new: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__8_2_0.opt.old: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__1_2_0.counters.old: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__1_2_0.opt.old: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__4k_2_0.opt.old: 4\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__0_2_0.counters.new: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__512_2_0.opt.new: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__0_2_0.opt.new: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__32k_2_0.counters.old: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__64_2_0.opt.new: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__512_2_0.counters.old: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__512_2_0.opt.new: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__8_2_0.counters.new: 5\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__1_2_0.counters.old: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__32k_2_0.opt.new: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__32k_2_0.opt.old: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__0_2_0.counters.new: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__4k_2_0.counters.new: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__64_2_0.counters.old: 3\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__4k_2_0.counters.old: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__8_2_0.opt.new: 3\n bm_fullstack_unary_ping_pong.BM_UnaryPingPong_InProcessCHTTP2_NoOpMutator_Server_AddInitialMetadata_RandomAsciiMetadata_10__1___0_0.opt.new: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__1_2_0.counters.new: 1\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__0_2_0.opt.old: 2\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__64_2_0.counters.new: 4\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_MinInProcessCHTTP2_NoOpMutator_NoOpMutator__4k_2_0.opt.old: 5\n bm_fullstack_streaming_ping_pong.BM_StreamingPingPongWithCoalescingApi_InProcessCHTTP2_NoOpMutator_NoOpMutator__0_2_0.opt.new: 3\n\n\n[microbenchmarks] No significant performance differences\n```"
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"body": "lgtm\n\njust check that we are not actually using this anywhere (tests or otherwise)\n"
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"body": "Based on the search result, it only exists in `series.py` and `test_series.py`. Both fixed.\n"
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"body": "gr8 merge when ready \n"
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{
"title": "Bishan Vegetarian",
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],
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} |
{
"name": "Dtube Coin",
"symbol": "DTUBE",
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},
"gen": {
"logo": "logo.png",
"hasDark": true,
"score": 19
}
} |
{
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{"artist": "Jel", "timestamp": "2011-08-15 09:40:27.776555", "similars": [], "tags": [], "track_id": "TRMXESV128F92D090B", "title": "Black Blues Electric"} |
{"artist": "Swell", "timestamp": "2011-08-11 02:21:24.655077", "similars": [], "tags": [["lonely walk", "100"]], "track_id": "TRYDOFP128F423ECA9", "title": "I Don't Think So"} |
{
"id": 2014,
"title": "Girls Bravo",
"url": "https://mangadex.org/manga/2014",
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"title": "To Love-Ru",
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{
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},
{
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},
{
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},
{
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},
{
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{
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{
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{
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{
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},
{
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},
{
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},
{
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{
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{
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{
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{
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{
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{
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{"id": "76583", "author": "عفيف الدين التلمساني", "abyat": [{"bayt": " أَيَا طَلْعَةَ القَمَرِ المُبْهِجِ وَيَا فِتْنَةَ المُسْتَهَامِ الشَّجِي\r", "sadr": " أَيَا طَلْعَةَ القَمَرِ المُبْهِجِ", "ajez": " وَيَا فِتْنَةَ المُسْتَهَامِ الشَّجِي\r"}, {"bayt": " بِمَا بَيْنَنَا مِنْ عُهُودِ الهَوَى إِذَا جُزْتِ جَيْرُوتَ بِي عَرِّجِ\r", "sadr": " بِمَا بَيْنَنَا مِنْ عُهُودِ الهَوَى", "ajez": " إِذَا جُزْتِ جَيْرُوتَ بِي عَرِّجِ\r"}, {"bayt": " بَنَفْسَجُ صُدْغَيْكَ قَدْ لاَحَ ليِ فَبَشَّرَني بالَّذِي أَرْتَجِي\r", "sadr": " بَنَفْسَجُ صُدْغَيْكَ قَدْ لاَحَ ليِ", "ajez": " فَبَشَّرَني بالَّذِي أَرْتَجِي\r"}, {"bayt": " فَإِنَّ البَنَفْسَجَ تَفْسِيرُهُ كَمَا قَدْ عَلِمْتُ بِنَفْسِي أَجيِ\r", "sadr": " فَإِنَّ البَنَفْسَجَ تَفْسِيرُهُ", "ajez": " كَمَا قَدْ عَلِمْتُ بِنَفْسِي أَجيِ\r"}, {"bayt": " فَكَمْ لَيْلَةٍ بِتُّ أَطْوِي يَدِي عَلَى كَبِدِي مِنْ جَوَىً مُنْضِجِ\r", "sadr": " فَكَمْ لَيْلَةٍ بِتُّ أَطْوِي يَدِي", "ajez": " عَلَى كَبِدِي مِنْ جَوَىً مُنْضِجِ\r"}, {"bayt": " فلاَ تَمْزِجَنْ بِدَمِي عَبْرَتِي وَرِيقُكَ لي بِالكُؤُوسِ امْزِجِ", "sadr": " فلاَ تَمْزِجَنْ بِدَمِي عَبْرَتِي", "ajez": " وَرِيقُكَ لي بِالكُؤُوسِ امْزِجِ"}], "era": "العصر الأندلسي", "title": "أَيَا طَلْعَةَ القَمَرِ المُبْهِجِ"} |
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{"body":"Martin Luther King, Jr. \"I Have A Dream\" World Peace Rose Garden Back view of Rose Garden NPS Photo The International World Peace Rose Gardens program is a worldwide effort to help youth recognize the importance and value of peace. In March 1992, the Martin Luther King, Jr. \"I Have a Dream\" World Peace Rose Garden was planted at the Martin Luther King, Jr. National Historic Site. The garden is an artistic interpretation of Dr. King’s life and ideals of peace through nonviolence. The garden’s starburst design brings attention to the brilliance of Dr. King’s ideals using the Official Flower of the United States, the rose. The Garden borders the Peace Plaza, in front of the Visitor Center. It has 185 roses in a variety of colors and fragrances. The graves of Dr. and Mrs. King can be seen directly across the street if you stand at the Peace Plaza, facing the rose garden. Each year an annual contest is held and students from local, national, and global schools submit poems of peace. Winning poems are selected and installed in the rose garden for the period of a year. There were a total of 27 \"Inspirational Messages of Peace\" for 2016. The Atlanta garden is one of five major World Peace Rose Gardens established around the world by International World Peace Rose Gardens. The other gardens are located at other cultural, historical and sacred centers of the world and focus on different aspects of peace. State Capitol Park, Sacramento, California Basilica of St. Francis of Assisi, Assisi, Italy Basilica of Our Lady of Guadalupe, Mexico City, Mexico Lake Shrine, Pacific Palisades, California Our Lady of Guadalupe Shrine, Sacramento, California","url":"https://www.nps.gov/malu/planyourvisit/mlk_jr_world_peace_rose_garden.htm","title":"Martin Luther King Jr National Historic Site: World Peace Rose Garden"} |
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{
"filename": "7770-multiple-step-greedy-policies-in-approximate-and-online-reinforcement-learning.pdf",
"metadata": {
"title": "Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning",
"author": "Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor",
"date": 2018,
"identifiers": {
"url": "https://papers.nips.cc/paper/7770-multiple-step-greedy-policies-in-approximate-and-online-reinforcement-learning.pdf"
},
"journal": "Conference on Neural Information Processing Systems",
"abstract": "Multiple-step lookahead policies have demonstrated high empirical competence in Reinforcement Learning, via the use of Monte Carlo Tree Search or Model Predictive Control. In a recent work [<a class=\"ref-link\" id=\"c5\" href=\"#r5\">5</a>], multiple-step greedy policies and their use in vanilla Policy Iteration algorithms were proposed and analyzed. In this work, we study multiple-step greedy algorithms in more practical setups. We begin by highlighting a counter-intuitive difficulty, arising with soft-policy updates: even in the absence of approximations, and contrary to the 1-step-greedy case, monotonic policy improvement is not guaranteed unless the update stepsize is sufficiently large. Taking particular care about this difficulty, we formulate and analyze online and approximate algorithms that use such a multi-step greedy operator."
},
"keywords": [
{
"term": "Markov Decision Process",
"url": "https://en.wikipedia.org/wiki/Markov_Decision_Process"
},
{
"term": "actor critic",
"url": "https://en.wikipedia.org/wiki/actor_critic"
},
{
"term": "dynamic programming",
"url": "https://en.wikipedia.org/wiki/dynamic_programming"
},
{
"term": "Reinforcement Learning",
"url": "https://en.wikipedia.org/wiki/Reinforcement_Learning"
},
{
"term": "model predictive control",
"url": "https://en.wikipedia.org/wiki/model_predictive_control"
}
],
"highlights": [
"The use of the 1-step policy improvement in Reinforcement Learning (RL) was theoretically investigated under several frameworks, e.g., Policy Iteration (PI) [<a class=\"ref-link\" id=\"c18\" href=\"#r18\">18</a>], approximate Policy Iteration [<a class=\"ref-link\" id=\"c2\" href=\"#r2\">2</a>, <a class=\"ref-link\" id=\"c9\" href=\"#r9\">9</a>, <a class=\"ref-link\" id=\"c13\" href=\"#r13\">13</a>], and Actor-Critic [<a class=\"ref-link\" id=\"c10\" href=\"#r10\">10</a>]; its practical uses are abundant [<a class=\"ref-link\" id=\"c22\" href=\"#r22\">22</a>, <a class=\"ref-link\" id=\"c12\" href=\"#r12\">12</a>, <a class=\"ref-link\" id=\"c25\" href=\"#r25\">25</a>]",
"We find this property intriguing since monotonic improvement is guaranteed in the case of soft updates w.r.t. the 1-step greedy policy, and is central to the analysis of many Reinforcement Learning algorithms [<a class=\"ref-link\" id=\"c10\" href=\"#r10\">10</a>, <a class=\"ref-link\" id=\"c9\" href=\"#r9\">9</a>, <a class=\"ref-link\" id=\"c22\" href=\"#r22\">22</a>]",
"Let \u03c0k be the policy at the k-th iteration of \u03ba-Approximate Policy Iteration and \u03b4 be the error as defined in Definition 2",
"Let \u03c3\u03ba,k be the policy at the k-th iteration of \u03ba-Policy Search by Dynamic Programming and \u03b4 be the error as defined in Definition 2",
"We introduced and analyzed online and approximate Policy Iteration methods, generalized to the \u03ba-greedy policy, an instance of a multiple-step greedy policy",
"The second property we find intriguing stemmed from analyzing \u03ba generalizations of known approximate hard-update Policy Iteration methods"
],
"key_statements": [
"The use of the 1-step policy improvement in Reinforcement Learning (RL) was theoretically investigated under several frameworks, e.g., Policy Iteration (PI) [<a class=\"ref-link\" id=\"c18\" href=\"#r18\">18</a>], approximate Policy Iteration [<a class=\"ref-link\" id=\"c2\" href=\"#r2\">2</a>, <a class=\"ref-link\" id=\"c9\" href=\"#r9\">9</a>, <a class=\"ref-link\" id=\"c13\" href=\"#r13\">13</a>], and Actor-Critic [<a class=\"ref-link\" id=\"c10\" href=\"#r10\">10</a>]; its practical uses are abundant [<a class=\"ref-link\" id=\"c22\" href=\"#r22\">22</a>, <a class=\"ref-link\" id=\"c12\" href=\"#r12\">12</a>, <a class=\"ref-link\" id=\"c25\" href=\"#r25\">25</a>]",
"We find this property intriguing since monotonic improvement is guaranteed in the case of soft updates w.r.t. the 1-step greedy policy, and is central to the analysis of many Reinforcement Learning algorithms [<a class=\"ref-link\" id=\"c10\" href=\"#r10\">10</a>, <a class=\"ref-link\" id=\"c9\" href=\"#r9\">9</a>, <a class=\"ref-link\" id=\"c22\" href=\"#r22\">22</a>]",
"We introduce two new concentrability coefficients suitable for bounding the worst-case performance of Policy Iteration algorithms with approximate \u03ba-greedy policies",
"Let \u03c0k be the policy at the k-th iteration of \u03ba-Approximate Policy Iteration and \u03b4 be the error as defined in Definition 2",
"Let \u03c3\u03ba,k be the policy at the k-th iteration of \u03ba-Policy Search by Dynamic Programming and \u03b4 be the error as defined in Definition 2",
"We introduced and analyzed online and approximate Policy Iteration methods, generalized to the \u03ba-greedy policy, an instance of a multiple-step greedy policy",
"The second property we find intriguing stemmed from analyzing \u03ba generalizations of known approximate hard-update Policy Iteration methods"
],
"summary": [
"The use of the 1-step policy improvement in Reinforcement Learning (RL) was theoretically investigated under several frameworks, e.g., Policy Iteration (PI) [<a class=\"ref-link\" id=\"c18\" href=\"#r18\">18</a>], approximate PI [<a class=\"ref-link\" id=\"c2\" href=\"#r2\">2</a>, <a class=\"ref-link\" id=\"c9\" href=\"#r9\">9</a>, <a class=\"ref-link\" id=\"c13\" href=\"#r13\">13</a>], and Actor-Critic [<a class=\"ref-link\" id=\"c10\" href=\"#r10\">10</a>]; its practical uses are abundant [<a class=\"ref-link\" id=\"c22\" href=\"#r22\">22</a>, <a class=\"ref-link\" id=\"c12\" href=\"#r12\">12</a>, <a class=\"ref-link\" id=\"c25\" href=\"#r25\">25</a>].",
"Recent work [<a class=\"ref-link\" id=\"c5\" href=\"#r5\">5</a>] provided guarantees on the performance of the multiple-step greedy policy and generalizations of it in PI.",
"We find this property intriguing since monotonic improvement is guaranteed in the case of soft updates w.r.t. the 1-step greedy policy, and is central to the analysis of many RL algorithms [<a class=\"ref-link\" id=\"c10\" href=\"#r10\">10</a>, <a class=\"ref-link\" id=\"c9\" href=\"#r9\">9</a>, <a class=\"ref-link\" id=\"c22\" href=\"#r22\">22</a>].",
"These algorithms assume access to a generative model (Section 5) or to an approximate multiple-step greedy policy (Section 6).",
"The set of \u03ba-greedy policies w.r.t. a value function v, G\u03ba(v), is defined using the following operators:",
"We focus on policy improvement of multiple-step greedy policies, performed with soft updates.",
"Soft updates of the 1-step greedy policy have proved necessary and beneficial in prominent algorithms [<a class=\"ref-link\" id=\"c10\" href=\"#r10\">10</a>, <a class=\"ref-link\" id=\"c9\" href=\"#r9\">9</a>, <a class=\"ref-link\" id=\"c22\" href=\"#r22\">22</a>].",
"Theorem 1 guarantees monotonic improvement for the 1-step greedy policy as a special case when \u03ba = 0.",
"To better understand this operator, first notice that in Stochastic Approximation methods such as Algorithm 1, the policy is improved using soft updates with decaying stepsizes.",
"Theorem 1 establishes the conditions required for guaranteed monotonic improvement of softlyupdated multiple-step greedy policies.",
"We introduce two new concentrability coefficients suitable for bounding the worst-case performance of PI algorithms with approximate \u03ba-greedy policies.",
"A natural generalization of API [<a class=\"ref-link\" id=\"c13\" href=\"#r13\">13</a>, <a class=\"ref-link\" id=\"c19\" href=\"#r19\">19</a>, <a class=\"ref-link\" id=\"c11\" href=\"#r11\">11</a>] to the multiple-step greedy policy is \u03ba-API, as given in Algorithm 2.",
"Let \u03c0k be the policy at the k-th iteration of \u03ba-API and \u03b4 be the error as defined in Definition 2.",
"As for the other extreme, \u03ba = 1, we first remind that in the non-approximate case, applying T\u03ba=1 amounts to solving the original \u03b3-discounted MDP in a single step [5, Remark 4].",
"Let \u03c3\u03ba,k be the policy at the k-th iteration of \u03ba-PSDP and \u03b4 be the error as defined in Definition 2.",
"Compared to \u03ba-API from the previous section, the \u03ba-PSDP bound consists of a different fixed approximation error and a shared geometrically decaying term.",
"We introduced and analyzed online and approximate PI methods, generalized to the \u03ba-greedy policy, an instance of a multiple-step greedy policy.",
"We discovered two intriguing properties compared to the well-studied 1-step greedy policy, which we believe can be impactful in designing state-of-the-art algorithms.",
"Successive application of multiple-step greedy policies with a soft, stepsize-based update does not guarantee improvement; see Theorem 1.",
"In the online case, Lemma 5.4 in [<a class=\"ref-link\" id=\"c10\" href=\"#r10\">10</a>] does not hold with multiple-step greedy policies."
],
"headline": "We study multiple-step greedy algorithms in more practical setups",
"reference_links": [
{
"id": "1",
"entry": "[1] J Andrew Bagnell, Sham M Kakade, Jeff G Schneider, and Andrew Y Ng. Policy search by dynamic programming. In Advances in neural information processing systems, pages 831\u2013838, 2004.",
"scholar_url": "https://scholar.google.co.uk/scholar?q=Bagnell%2C%20J.Andrew%20Kakade%2C%20Sham%20M.%20Schneider%2C%20Jeff%20G.%20and%20Andrew%20Y%20Ng.%20Policy%20search%20by%20dynamic%20programming%202004",
"oa_query": "https://api.scholarcy.com/oa_version?query=Bagnell%2C%20J.Andrew%20Kakade%2C%20Sham%20M.%20Schneider%2C%20Jeff%20G.%20and%20Andrew%20Y%20Ng.%20Policy%20search%20by%20dynamic%20programming%202004"
},
{
"id": "2",
"entry": "[2] Dimitri P Bertsekas and John N Tsitsiklis. Neuro-dynamic programming: an overview. In Decision and Control, 1995., Proceedings of the 34th IEEE Conference on, volume 1, pages 560\u2013564. IEEE, 1995.",
"scholar_url": "https://scholar.google.co.uk/scholar?q=Bertsekas%2C%20Dimitri%20P.%20Tsitsiklis%2C%20John%20N.%20Neuro-dynamic%20programming%3A%20an%20overview%201995",
"oa_query": "https://api.scholarcy.com/oa_version?query=Bertsekas%2C%20Dimitri%20P.%20Tsitsiklis%2C%20John%20N.%20Neuro-dynamic%20programming%3A%20an%20overview%201995"
},
{
"id": "3",
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Bouzy%2C%20Bruno%20Helmstetter%2C%20Bernard%20Monte-carlo%20go%20developments",
"oa_query": "https://api.scholarcy.com/oa_version?query=Bouzy%2C%20Bruno%20Helmstetter%2C%20Bernard%20Monte-carlo%20go%20developments"
},
{
"id": "4",
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Browne%2C%20Cameron%20B.%20Powley%2C%20Edward%20Whitehouse%2C%20Daniel%20Lucas%2C%20Simon%20M.%20A%20survey%20of%20monte%20carlo%20tree%20search%20methods%202012",
"oa_query": "https://api.scholarcy.com/oa_version?query=Browne%2C%20Cameron%20B.%20Powley%2C%20Edward%20Whitehouse%2C%20Daniel%20Lucas%2C%20Simon%20M.%20A%20survey%20of%20monte%20carlo%20tree%20search%20methods%202012"
},
{
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},
{
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"oa_query": "https://api.scholarcy.com/oa_version?query=Ernst%2C%20Damien%20Glavic%2C%20Mevludin%20Capitanescu%2C%20Florin%20Wehenkel%2C%20Louis%20Reinforcement%20learning%20versus%20model%20predictive%20control%3A%20a%20comparison%20on%20a%20power%20system%20problem%202009"
},
{
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"oa_query": "https://api.scholarcy.com/oa_version?query=Amir-massoud%20Farahmand%2C%20Csaba%20Szepesv%C3%A1ri%20Munos%2C%20R%C3%A9mi%20Error%20propagation%20for%20approximate%20policy%20and%20value%20iteration%202010"
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{
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"oa_query": "https://api.scholarcy.com/oa_version?query=Jiang%2C%20Nan%20Kulesza%2C%20Alex%20Singh%2C%20Satinder%20Lewis%2C%20Richard%20The%20dependence%20of%20effective%20planning%20horizon%20on%20model%20accuracy%202015"
},
{
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"oa_query": "https://api.scholarcy.com/oa_version?query=Kakade%2C%20S.M.%20Langford%2C%20J.%20Approximately%20Optimal%20Approximate%20Reinforcement%20Learning%202002"
},
{
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"oa_query": "https://api.scholarcy.com/oa_version?query=Konda%2C%20Vijaymohan%20R.%20Borkar%2C%20Vivek%20S.%20Actor-critic%E2%80%93type%20learning%20algorithms%20for%20markov%20decision%20processes%201999"
},
{
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"oa_query": "https://api.scholarcy.com/oa_version?query=Alessandro%20Lazaric%2C%20Mohammad%20Ghavamzadeh%20Munos%2C%20R%C3%A9mi%20Analysis%20of%20classificationbased%20policy%20iteration%20algorithms%202016"
},
{
"id": "12",
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"oa_query": "https://api.scholarcy.com/oa_version?query=Mnih%2C%20Volodymyr%20Badia%2C%20Adria%20Puigdomenech%20Mirza%2C%20Mehdi%20Graves%2C%20Alex%20Asynchronous%20methods%20for%20deep%20reinforcement%20learning%202016"
},
{
"id": "13",
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Munos%2C%20R%C3%A9mi%20Error%20bounds%20for%20approximate%20policy%20iteration%202003",
"oa_query": "https://api.scholarcy.com/oa_version?query=Munos%2C%20R%C3%A9mi%20Error%20bounds%20for%20approximate%20policy%20iteration%202003"
},
{
"id": "14",
"entry": "[14] R\u00e9mi Munos. Performance bounds in l_p-norm for approximate value iteration. SIAM journal on control and optimization, 46(2):541\u2013561, 2007.",
"scholar_url": "https://scholar.google.co.uk/scholar?q=Munos%2C%20R%C3%A9mi%20Performance%20bounds%20in%20l_p-norm%20for%20approximate%20value%20iteration%202007",
"oa_query": "https://api.scholarcy.com/oa_version?query=Munos%2C%20R%C3%A9mi%20Performance%20bounds%20in%20l_p-norm%20for%20approximate%20value%20iteration%202007"
},
{
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"oa_query": "https://api.scholarcy.com/oa_version?query=Negenborn%2C%20Rudy%20R.%20Schutter%2C%20Bart%20De%20Wiering%2C%20Marco%20A.%20Hellendoorn%2C%20Hans%20Learningbased%20model%20predictive%20control%20for%20markov%20decision%20processes%202005"
},
{
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"oa_query": "https://api.scholarcy.com/oa_version?query=Perkins%2C%20Steven%20Leslie%2C%20David%20S.%20Asynchronous%20stochastic%20approximation%20with%20differential%20inclusions%202013"
},
{
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"oa_query": "https://api.scholarcy.com/oa_version?query=Petrik%2C%20Marek%20Scherrer%2C%20Bruno%20Biasing%20approximate%20dynamic%20programming%20with%20a%20lower%20discount%20factor%202009"
},
{
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},
{
"id": "19",
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Scherrer%2C%20Bruno%20Approximate%20policy%20iteration%20schemes%3A%20a%20comparison%202014",
"oa_query": "https://api.scholarcy.com/oa_version?query=Scherrer%2C%20Bruno%20Approximate%20policy%20iteration%20schemes%3A%20a%20comparison%202014"
},
{
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Scherrer%2C%20Bruno%20Improved%20and%20Generalized%20Upper%20Bounds%20on%20the%20Complexity%20of%20Policy%20Iteration%202016-02",
"oa_query": "https://api.scholarcy.com/oa_version?query=Scherrer%2C%20Bruno%20Improved%20and%20Generalized%20Upper%20Bounds%20on%20the%20Complexity%20of%20Policy%20Iteration%202016-02"
},
{
"id": "21",
"entry": "[21] Bruno Scherrer and Matthieu Geist. Local policy search in a convex space and conservative policy iteration as boosted policy search. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 35\u201350.",
"scholar_url": "https://scholar.google.co.uk/scholar?q=Scherrer%2C%20Bruno%20Geist%2C%20Matthieu%20Local%20policy%20search%20in%20a%20convex%20space%20and%20conservative%20policy%20iteration%20as%20boosted%20policy%20search",
"oa_query": "https://api.scholarcy.com/oa_version?query=Scherrer%2C%20Bruno%20Geist%2C%20Matthieu%20Local%20policy%20search%20in%20a%20convex%20space%20and%20conservative%20policy%20iteration%20as%20boosted%20policy%20search"
},
{
"id": "22",
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Schulman%2C%20John%20Levine%2C%20Sergey%20Abbeel%2C%20Pieter%20Jordan%2C%20Michael%20Trust%20region%20policy%20optimization%202015",
"oa_query": "https://api.scholarcy.com/oa_version?query=Schulman%2C%20John%20Levine%2C%20Sergey%20Abbeel%2C%20Pieter%20Jordan%2C%20Michael%20Trust%20region%20policy%20optimization%202015"
},
{
"id": "23",
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Sheppard%2C%20Brian%20World-championship-caliber%20scrabble%202002",
"oa_query": "https://api.scholarcy.com/oa_version?query=Sheppard%2C%20Brian%20World-championship-caliber%20scrabble%202002"
},
{
"id": "24",
"entry": "[24] David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815, 2017.",
"arxiv_url": "https://arxiv.org/pdf/1712.01815"
},
{
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Silver%2C%20David%20Schrittwieser%2C%20Julian%20Simonyan%2C%20Karen%20Antonoglou%2C%20Ioannis%20Mastering%20the%20game%20of%20go%20without%20human%20knowledge%202017",
"oa_query": "https://api.scholarcy.com/oa_version?query=Silver%2C%20David%20Schrittwieser%2C%20Julian%20Simonyan%2C%20Karen%20Antonoglou%2C%20Ioannis%20Mastering%20the%20game%20of%20go%20without%20human%20knowledge%202017"
},
{
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Strehl%2C%20Alexander%20L.%20Li%2C%20Lihong%20Littman%2C%20Michael%20L.%20Reinforcement%20learning%20in%20finite%20mdps%3A%20Pac%20analysis%202009",
"oa_query": "https://api.scholarcy.com/oa_version?query=Strehl%2C%20Alexander%20L.%20Li%2C%20Lihong%20Littman%2C%20Michael%20L.%20Reinforcement%20learning%20in%20finite%20mdps%3A%20Pac%20analysis%202009"
},
{
"id": "27",
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Tamar%2C%20Aviv%20Thomas%2C%20Garrett%20Zhang%2C%20Tianhao%20Levine%2C%20Sergey%20Learning%20from%20the%20hindsight%20plan%E2%80%94episodic%20mpc%20improvement%202017",
"oa_query": "https://api.scholarcy.com/oa_version?query=Tamar%2C%20Aviv%20Thomas%2C%20Garrett%20Zhang%2C%20Tianhao%20Levine%2C%20Sergey%20Learning%20from%20the%20hindsight%20plan%E2%80%94episodic%20mpc%20improvement%202017"
},
{
"id": "28",
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Tesauro%2C%20Gerald%20Galperin%2C%20Gregory%20R.%20On-line%20policy%20improvement%20using%20monte-carlo%20search%201997",
"oa_query": "https://api.scholarcy.com/oa_version?query=Tesauro%2C%20Gerald%20Galperin%2C%20Gregory%20R.%20On-line%20policy%20improvement%20using%20monte-carlo%20search%201997"
},
{
"id": "29",
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"scholar_url": "https://scholar.google.co.uk/scholar?q=Christopher%20JCH%20Watkins%20and%20Peter%20Dayan%20Qlearning%20Machine%20learning%20834279292%201992",
"oa_query": "https://api.scholarcy.com/oa_version?query=Christopher%20JCH%20Watkins%20and%20Peter%20Dayan%20Qlearning%20Machine%20learning%20834279292%201992"
}
]
}
|
매핑
PUT child_example
{
"mappings": {
"doc": {
"properties": {
"join": {
"type": "join",
"relations": {
"question": "answer"
}
}
}
}
}
}
부모입력 (question)
PUT child_example/doc/1
{
"join": {
"name": "question"
},
"body": "<p>I have Windows 2003 server and i bought a new Windows 2008 server...",
"title": "Whats the best way to file transfer my site from server to a newer one?",
"tags": [
"windows-server-2003",
"windows-server-2008",
"file-transfer"
]
}
자식입력 (answer)
PUT child_example/doc/2?routing=1
{
"join": {
"name": "answer",
"parent": "1"
},
"owner": {
"location": "Norfolk, United Kingdom",
"display_name": "Sam",
"id": 48
},
"body": "<p>Unfortunately you're pretty much limited to FTP...",
"creation_date": "2009-05-04T13:45:37.030"
}
PUT child_example/doc/3?routing=1&refresh
{
"join": {
"name": "answer",
"parent": "1"
},
"owner": {
"location": "Norfolk, United Kingdom",
"display_name": "Troll",
"id": 49
},
"body": "<p>Use Linux...",
"creation_date": "2009-05-05T13:45:37.030"
}
child-agg 검색
POST child_example/_search?size=0
{
"size" : 0,
"aggs": {
"top-tags": {
"terms": {
"field": "tags.keyword",
"size": 10
},
"aggs": {
"to-answers": {
"children": {
"type" : "answer"
},
"aggs": {
"top-names": {
"terms": {
"field": "owner.display_name.keyword",
"size": 10
}
}
}
}
}
}
}
}
|
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{"value": 2087383, "isPrime": true},
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{"value": 2087399, "isPrime": true},
{"value": 2087419, "isPrime": true},
{"value": 2087443, "isPrime": true},
{"value": 2087453, "isPrime": true},
{"value": 2087461, "isPrime": true},
{"value": 2087467, "isPrime": true},
{"value": 2087483, "isPrime": true},
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{"value": 2087497, "isPrime": true},
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{"value": 2087543, "isPrime": true},
{"value": 2087551, "isPrime": true},
{"value": 2087557, "isPrime": true},
{"value": 2087563, "isPrime": true},
{"value": 2087593, "isPrime": true},
{"value": 2087627, "isPrime": true},
{"value": 2087669, "isPrime": true},
{"value": 2087671, "isPrime": true},
{"value": 2087689, "isPrime": true},
{"value": 2087711, "isPrime": true},
{"value": 2087713, "isPrime": true},
{"value": 2087717, "isPrime": true},
{"value": 2087741, "isPrime": true},
{"value": 2087759, "isPrime": true},
{"value": 2087773, "isPrime": true},
{"value": 2087791, "isPrime": true},
{"value": 2087801, "isPrime": true},
{"value": 2087807, "isPrime": true},
{"value": 2087809, "isPrime": true},
{"value": 2087831, "isPrime": true},
{"value": 2087837, "isPrime": true},
{"value": 2087857, "isPrime": true},
{"value": 2087861, "isPrime": true},
{"value": 2087879, "isPrime": true},
{"value": 2087903, "isPrime": true},
{"value": 2087909, "isPrime": true},
{"value": 2087927, "isPrime": true},
{"value": 2087941, "isPrime": true},
{"value": 2087983, "isPrime": true},
{"value": 2087993, "isPrime": true},
{"value": 2087999, "isPrime": true}
]
|
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{
"name": "galeforce",
"author": {
"name": "Brandon Cho",
"email": "brandon.cho@gmail.com",
"url": "https://github.com/bcho04"
},
"description": "A customizable, promise-based, and command-oriented TypeScript library for the Riot Games API.",
"keywords": [
"galeforce",
"api",
"riot",
"lol",
"tft",
"lor",
"valorant",
"league of legends",
"teamfight tactics",
"legends of runeterra",
"riot games",
"data dragon",
"ddragon"
],
"homepage": "https://github.com/bcho04/galeforce",
"repository": {
"type": "git",
"url": "https://github.com/bcho04/galeforce.git"
},
"bugs": "https://github.com/bcho04/galeforce/issues",
"version": "0.4.0",
"license": "Apache-2.0",
"main": "./dist/index.js",
"types": "./dist/index.d.ts",
"engines": {
"node": ">=10.13.0"
},
"dependencies": {
"ajv": "^8.6.0",
"axios": "^0.21.1",
"bottleneck": "^2.19.5",
"chalk": "^4.1.1",
"debug": "^4.3.1",
"lodash": "^4.17.21",
"redis": "^3.1.2",
"support-color": "^7.1.0",
"typescript-json-schema": "^0.50.1",
"uuid": "^8.3.2",
"yaml": "^1.10.2"
},
"devDependencies": {
"@types/chai": "^4.2.19",
"@types/debug": "^4.1.5",
"@types/lodash": "^4.14.170",
"@types/mocha": "^8.2.2",
"@types/node": "^15.12.4",
"@types/redis": "^2.8.29",
"@types/uuid": "^8.3.0",
"@typescript-eslint/eslint-plugin": "^4.28.0",
"@typescript-eslint/parser": "^4.28.0",
"chai": "^4.3.4",
"chai-as-promised": "^7.1.1",
"eslint": "^7.29.0",
"eslint-config-airbnb-base": "^14.2.1",
"eslint-plugin-import": "^2.23.4",
"mocha": "^9.0.1",
"nock": "^13.1.0",
"typedoc": "^0.19.2",
"typedoc-plugin-external-module-name": "^4.0.6",
"typescript": "^4.2.4"
},
"scripts": {
"lint": "npx eslint --ext .ts,.tsx,.js,.jsx ./src ./scripts",
"lint:fix": "npx eslint --ext .ts,.tsx,.js,.jsx ./src ./scripts --fix",
"build": "npx tsc --project tsconfig.json",
"test": "mocha --exit",
"postinstall": "node ./scripts/postinstall.js",
"docs": "npx typedoc"
}
}
|
{
"Dialogue_ID": "132",
"Emotion": "neutral",
"EndTime": "00:14:32,067",
"Episode": "23",
"Season": "8",
"Sentiment": "neutral",
"Speaker": "Nurse",
"SrNo": "1300",
"StartTime": "00:14:29,273",
"Utterance": "I’m sorry, that information is restricted to hospital staff",
"Utterance_ID": "5"
} |
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"short_title": "Victims of Crime Act of 1979",
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],
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"date": "1980-02-13",
"text": "(Reported to House from the Committee on the Judiciary with amendment, H. Rept. 96-753) Victims of Crime Act of 1979 - Directs the Attorney General to make grants to qualifying State Programs for the compensation of victims of crime. States that grants made under this Act shall total 25 percent of the allowable compensation paid to victims and dependent survivors of victims of State crimes designated by the State as appropriate for compensation under this program and 100 percent of the allowable compensation paid to victims and dependent survivors of victims of crimes which would constitute designated State crimes except for the fact that the crime is subject to exclusive jurisdiction. Establishes an Advisory Committee on Victims of Crime to advise the Attorney General with respect to the administration of this Act and the compensation of victims of crime. Requires that such programs: (1) offer compensation for personal injuries suffered as a result of qualifying State and Federal crimes; (2) offer compensation to surviving dependents of persons whose deaths resulted from such crimes; (3) grant claimants the right to a hearing; (4) require cooperation with law enforcement agencies as a condition for recovery; (5) do not require claimants to accept welfare benefits; (6) require denial or reduction of a claim where the victim contributes to the injury suffered; and (7) do not require that any person be apprehended or convicted of the qualifying crime that gave rise to the claim. Requires such States to have in effect laws which: (1) subrogate the State to any claim the victim or dependent has against the perpetrator to the extent of the amount compensated by the State; (2) require that restitution be made, where appropriate, by the perpetrator of the crime to the victim; (3) require proceeds earned for interviews or articles by an individual charged or convicted of a crime to be deposited in escrow for the benefit of the victims of such crime; (4) require law enforcement agencies to take reasonable care to inform victims of qualifying crimes of compensation programs; and (5) assess upon persons convicted of qualifying crimes court costs of at least five dollars, to be paid to the fund from which the State pays victim compensation awards. Authorizes appropriations to carry out this Act in the amounts of: $15,000,000 for fiscal year 1980, $25,000,000 for fiscal year 1981, and $35,000,000 for fiscal year 1982."
},
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],
"updated_at": "2013-02-02T18:44:14-05:00"
} |
[
"/ Updated By Tom Winter and Robert Windrem\n\nThe Russian banker Trump son-in-law Jared Kushner met with in December is viewed by U.S. intelligence as a \"Putin crony\" and a graduate of a \"finishing school\" for spies who was often tasked with sensitive financial operations by Putin, according to multiple U.S. officials and documents viewed by NBC News.",
"Evgeny Buryakov, a former New York banker who was convicted in federal court of conspiring to act in the United States as an agent of the Russian Federation, is shown in this handout photo sitting on a commercial flight, escorted by deportation officers and turned over to Russian authorities, April 5, 2017.",
"Related: Jared Kushner Under Scrutiny in Russia Probe, Say Officials\n\nNBC News reported Thursday that Kushner, the president's son-in-law and one of his senior advisers, has come under FBI scrutiny in the Russia investigation, according to multiple U.S. officials.",
"Ivanka Trump, daughter of President Donald Trump, with her husband Jared Kushner, talk to Attorney General-designate Sen. Jeff Sessions, R-Ala., during the inaugural luncheon in honor of President Donald Trump, Friday, Jan. 20, 2017 on Capitol Hill in Washington.",
"The U.S. did not cite any particular transactions when it sanctioned the bank, but said instead that Russia’s failure to meet \"basic standards of international conduct\" warranted onerous sanctions.",
"Gorkov has also been awarded the \"Certificate of Honor of the Government of the Russian Federation\" and the \"Medal of the Order of Merit for Services to the Fatherland,\" both presented by Putin.",
"Sberbank and VEB, Gorkov’s two banks, have been critical to the Russian economy, but have also engaged in high-profile bailouts of Russian companies, including those owned by favored oligarchs.",
"Details of what they discussed have not been released, although Kushner’s lawyer, Jamie Gorelick, said Kushner is prepared to cooperate with congressional investigators or the FBI if asked.",
"According to Reuters, Gorkov met \"with a number of representatives of the largest banks and business establishments of the U.S., including Jared Kushner, the head of Kushner Companies.\"",
"Both the White House and VEB confirm that Kushner and Gorkov met at a banking \"road show\" but haven’t disclosed either the location for the meeting or the specific date in December.",
"As the Russian state national development bank, VEB has played a critical role in blunting the impact of U.S. sanctions against Russia by finding other sources of foreign capital.",
"It is not known what has drawn FBI scrutiny, but congressional aides have said they would like to question Kushner about a meeting Kushner had with Gorkov in December 2016.",
"\"Because Gorkov is former FSB doesn’t mean that he still is, but Putin uses the security state — the FSB and military intelligence — as an extension of his rule.\"",
"According to court records reviewed by NBC News, Buryakov used his position to spy on unnamed U.S. companies, and sought information on high-frequency trading.",
"Buryakov claimed he had diplomatic immunity in his defense, which was paid by VEB, but after a court ruled he didn’t qualify, he pleaded guilty in March 2016.",
"Related: Flynn, Manafort Are Key Figures in Russia Probe Mueller Will Lead\n\nGorkov is close to Putin, \"a Putin crony,\" as one official put it.",
"The White House subsequently characterized the meeting as part of Kushner’s role as a transition adviser and conduit for the State Department.",
"They have also played a major role in attempts to mitigate the U.S. sanctions that were imposed on Russia following its annexation of Crimea.",
"Before that, Gorkov was the deputy chairman of Sberbank, Russia’s biggest bank, also state-owned, and also under U.S. sanctions since 2014.",
"Putin has long called on former Soviet and Russian intelligence operatives to play critical roles in Russian political and economic life.",
"Reuters\n\nPutin authorized a $22 billion of state financing for VEB to cover bad debts built up since the imposition of sanctions.",
"Sergey Gorkov, 48, graduated from the FSB Academy, which was chartered in 1994 to educate Russian Intelligence personnel.",
"VEB was also implicated in an espionage case that began before Gorkov took over 2016 but continued after he took over.",
"Prosecutors said his internet searches included \"NYC Homeland Security\" and \"New York City critical infrastructure.\"",
"One of its New York-based employees, Evgeny Buryakov, was arrested on espionage charges in January 2015.",
"Investigators believe Kushner has significant information relevant to the Russia probe, officials said.",
"Manuel Balce Ceneta / AP file\n\n\"It is the talent pool [Putin] draws from,\" said one U.S. official.",
"The U.S. cited the banks’ record of securing medium- and long-term U.S. sources of financing.",
"At the time, Kushner was an adviser to President-elect Trump and Gorkov was chairman of VEB.",
"Most recently, Putin chose him to head of the state-owned VneshEconomBank (VEB).",
"He has long served Russian President Vladimir Putin in critical economic roles.",
"The banks have responded by seeking funds from other sources, including China.",
"But Gorkov in a written statement to Reuters, said it was a business meeting.",
"That does not mean they suspect him of a crime or intend to charge him.",
"Sanctions severely limited their ability to raise funds in the U.S.",
"Breaking News Emails Get breaking news alerts and special reports.",
"The news and stories that matter, delivered weekday mornings.",
"He was sentenced to 30 months in prison and a $100,000 fine.",
"VEB was sanctioned in July 2014, Sberbank two months later.",
"He returned to Russia last month after being released.",
"The European Union later joined in the sanctions.",
"\"It is the talent pool [Putin] draws from.\"",
"Gorkov was not sanctioned personally."
] |
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