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"value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "www.frontiersin.org Yao Zheng", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "www.frontiersin.org Yao Zheng"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "152", "childIds": ["436", "437"], "backendDOMNodeId": 613, "browsergym_id": "9ade4746-3e44-4a3b"}, {"nodeId": "231", "ignored": false, "role": {"type": "role", "value": "listitem"}, "chromeRole": {"type": "internalRole", "value": 115}, "name": {"type": "computedString", "value": "", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "attribute", "attribute": "title"}]}, "properties": [{"name": "level", "value": {"type": "integer", "value": 1}}], "parentId": "153", "childIds": ["438", "439"], "backendDOMNodeId": 617, "browsergym_id": "efce76a3-bd29-4fc7"}, {"nodeId": "232", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "The ability to manipulate fluids has always been one of the focuses of scientific research and engineering application. The rapid development of machine learning technology provides a new perspective and method for active flow control. This review presents recent progress in combining reinforcement learning with high-dimensional, non-linear, and time-delay physical information. Compared with model-based closed-loop control methods, deep reinforcement learning (DRL) avoids modeling the complex flow system and effectively provides an intelligent end-to-end policy exploration paradigm. At the same time, there is no denying that obstacles still exist on the way to practical application. We have listed some challenges and corresponding advanced solutions. This review is expected to offer a deeper insight into the current state of DRL-based active flow control within fluid mechanics and inspires more non-traditional thinking for engineering.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "The ability to manipulate fluids has always been one of the focuses of scientific research and engineering application. The rapid development of machine learning technology provides a new perspective and method for active flow control. This review presents recent progress in combining reinforcement learning with high-dimensional, non-linear, and time-delay physical information. Compared with model-based closed-loop control methods, deep reinforcement learning (DRL) avoids modeling the complex flow system and effectively provides an intelligent end-to-end policy exploration paradigm. At the same time, there is no denying that obstacles still exist on the way to practical application. We have listed some challenges and corresponding advanced solutions. This review is expected to offer a deeper insight into the current state of DRL-based active flow control within fluid mechanics and inspires more non-traditional thinking for engineering."}}]}, "properties": [], "parentId": "154", "childIds": [], "backendDOMNodeId": 621}, {"nodeId": "233", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "Introduction", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Introduction"}}]}, "properties": [], "parentId": "157", "childIds": [], "backendDOMNodeId": 626}, {"nodeId": "234", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "Despite many successful research efforts in the past decades, modifying the dynamics of flows to induce and enforce desired behavior remains an open scientific problem. In many industrial fields, researchers have placed great expectations on flow control techniques for engineering goals [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Despite many successful research efforts in the past decades, modifying the dynamics of flows to induce and enforce desired behavior remains an open scientific problem. In many industrial fields, researchers have placed great expectations on flow control techniques for engineering goals ["}}]}, "properties": [], "parentId": "158", "childIds": [], "backendDOMNodeId": 628}, {"nodeId": "235", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "1", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "1"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "158", "childIds": ["440"], "backendDOMNodeId": 629, "browsergym_id": "df0b62c5-0315-4f89"}, {"nodeId": "236", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "\u2013", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "\u2013"}}]}, "properties": [], "parentId": "158", "childIds": [], "backendDOMNodeId": 631}, {"nodeId": "237", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "3", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "3"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "158", "childIds": ["441"], "backendDOMNodeId": 632, "browsergym_id": "789c59f7-bdb1-4e4b"}, {"nodeId": "238", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "], such as drag reduction, noise suppression, mixing enhancement, energy harvesting. Due to the aggravation of carbon emissions and the greenhouse effect, controlling transportation drag or aerodynamic lift has become increasingly imperative.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "], such as drag reduction, noise suppression, mixing enhancement, energy harvesting. Due to the aggravation of carbon emissions and the greenhouse effect, controlling transportation drag or aerodynamic lift has become increasingly imperative."}}]}, "properties": [], "parentId": "158", "childIds": [], "backendDOMNodeId": 634}, {"nodeId": "239", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "Driven by the urgent demand from industry, active flow control (AFC) is being developed rapidly to harvest benefits for aviation or marine. As shown in ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Driven by the urgent demand from industry, active flow control (AFC) is being developed rapidly to harvest benefits for aviation or marine. As shown in "}}]}, "properties": [], "parentId": "159", "childIds": [], "backendDOMNodeId": 636}, {"nodeId": "240", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "Figure 1", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "Figure 1"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "159", "childIds": ["442"], "backendDOMNodeId": 637, "browsergym_id": "c2651d3b-2f88-4a87"}, {"nodeId": "241", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ", Boeing and NASA tested a pneumatic sweeping-jet-based active flow control system on the vertical tail of the modified Boeing 757 ecoDemonstrator in April 2015. Active flow control was used to enhance the control authority of the rudder by mitigating flow separation on it at high rudder deflection, and side slip angles, which provided the required level of rudder control authority from a physically smaller vertical tail [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ", Boeing and NASA tested a pneumatic sweeping-jet-based active flow control system on the vertical tail of the modified Boeing 757 ecoDemonstrator in April 2015. Active flow control was used to enhance the control authority of the rudder by mitigating flow separation on it at high rudder deflection, and side slip angles, which provided the required level of rudder control authority from a physically smaller vertical tail ["}}]}, "properties": [], "parentId": "159", "childIds": [], "backendDOMNodeId": 639}, {"nodeId": "242", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "4", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "4"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "159", "childIds": ["443"], "backendDOMNodeId": 640, "browsergym_id": "a3875df8-6d63-4686"}, {"nodeId": "243", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. Whether using fluidic [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. Whether using fluidic ["}}]}, "properties": [], "parentId": "159", "childIds": [], "backendDOMNodeId": 642}, {"nodeId": "244", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "5", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "5"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "159", "childIds": ["444"], "backendDOMNodeId": 643, "browsergym_id": "fd79dd14-f731-4627"}, {"nodeId": "245", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "], micro blowing [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "], micro blowing ["}}]}, "properties": [], "parentId": "159", "childIds": [], "backendDOMNodeId": 645}, {"nodeId": "246", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "6", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "6"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "159", "childIds": ["445"], "backendDOMNodeId": 646, "browsergym_id": "84c82e48-430c-4388"}, {"nodeId": "247", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "] or plasma actuators [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "] or plasma actuators ["}}]}, "properties": [], "parentId": "159", "childIds": [], "backendDOMNodeId": 648}, {"nodeId": "248", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "7", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "7"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "159", "childIds": ["446"], "backendDOMNodeId": 649, "browsergym_id": "6569affd-7491-4212"}, {"nodeId": "249", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "], the critical problem of active flow control is to design a reasonable control policy. The predetermined open-loop manner is the most straightforward choice. Still, the external actuation might be invalid if the evolution deviates from expectations and there are no corrective feedback mechanisms to modify the policy to compensate [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "], the critical problem of active flow control is to design a reasonable control policy. The predetermined open-loop manner is the most straightforward choice. Still, the external actuation might be invalid if the evolution deviates from expectations and there are no corrective feedback mechanisms to modify the policy to compensate ["}}]}, "properties": [], "parentId": "159", "childIds": [], "backendDOMNodeId": 651}, {"nodeId": "250", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "8", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "8"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "159", "childIds": ["447"], "backendDOMNodeId": 652, "browsergym_id": "66fde0a8-731c-4b84"}, {"nodeId": "251", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ", ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ", "}}]}, "properties": [], "parentId": "159", "childIds": [], "backendDOMNodeId": 654}, {"nodeId": "252", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "9", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "9"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "159", "childIds": ["448"], "backendDOMNodeId": 655, "browsergym_id": "8bba687d-ee23-438e"}, {"nodeId": "253", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. A practical alternative is to adopt the closed-loop control manner [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. A practical alternative is to adopt the closed-loop control manner ["}}]}, "properties": [], "parentId": "159", "childIds": [], "backendDOMNodeId": 657}, {"nodeId": "254", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "10", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "10"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "159", "childIds": ["449"], "backendDOMNodeId": 658, "browsergym_id": "099fba46-19e6-4423"}, {"nodeId": "255", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "\u2013", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "\u2013"}}]}, "properties": [], "parentId": "159", "childIds": [], "backendDOMNodeId": 660}, {"nodeId": "256", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "12", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "12"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "159", "childIds": ["450"], "backendDOMNodeId": 661, "browsergym_id": "9e7e52b9-5819-439d"}, {"nodeId": "257", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "], where the response is continuous compared with the desired result. Specifically, the control output to the process is informed by the sensors recording the flow information, then modified and adjusted to reduce the deviation, thus forcing the response to follow the reference.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "], where the response is continuous compared with the desired result. Specifically, the control output to the process is informed by the sensors recording the flow information, then modified and adjusted to reduce the deviation, thus forcing the response to follow the reference."}}]}, "properties": [], "parentId": "159", "childIds": [], "backendDOMNodeId": 663}, {"nodeId": "258", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "FIGURE 1", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "FIGURE 1"}}]}, "properties": [], "parentId": "161", "childIds": [], "backendDOMNodeId": 666}, {"nodeId": "259", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "www.frontiersin.org", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "www.frontiersin.org"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "162", "childIds": ["451"], "backendDOMNodeId": 668, "browsergym_id": "5ad71047-00ba-4923"}, {"nodeId": "260", "ignored": false, "role": {"type": "role", "value": "paragraph"}, "chromeRole": {"type": "internalRole", "value": 133}, "name": {"type": "computedString", "value": "", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "attribute", "attribute": "title"}]}, "properties": [], "parentId": "162", "childIds": ["452", "453", "454", "455"], "backendDOMNodeId": 670, "browsergym_id": "fa8200fd-fc54-4362"}, {"nodeId": "261", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "In both ways, extensive work has been carried out by numerical simulations and experiments on exploring the nonlinear dynamics and underlying physical mechanisms of the controlled system with effective control law. For example, Xu et al. [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "In both ways, extensive work has been carried out by numerical simulations and experiments on exploring the nonlinear dynamics and underlying physical mechanisms of the controlled system with effective control law. For example, Xu et al. 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It was found that the Co-flow wall Jet had a mechanism to grow its control capability with the increasing adverse pressure gradient. Sato et al. [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "] investigated the separation control mechanism of a Co-flow Wall Jet, which utilized an upstream tangential injection and downstream streamwise suction simultaneously to achieve zero net-mass-flux flow control. It was found that the Co-flow wall Jet had a mechanism to grow its control capability with the increasing adverse pressure gradient. Sato et al. ["}}]}, "properties": [], "parentId": "165", "childIds": [], "backendDOMNodeId": 683}, {"nodeId": "264", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "14", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "14"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "165", "childIds": ["457"], "backendDOMNodeId": 684, "browsergym_id": "f9250e39-acd0-4e69"}, {"nodeId": "265", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "] conducted large-eddy simulations to study the separated flow control mechanism by a dielectric barrier discharge plasma actuator. From flow analysis, it was seen that an earlier and smoother transition case showed more significant improvements in the lift and drag coefficients. Moreover, the lift coefficient was improved since the actuation induced a large-scale vortex-shedding phenomenon.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "] conducted large-eddy simulations to study the separated flow control mechanism by a dielectric barrier discharge plasma actuator. From flow analysis, it was seen that an earlier and smoother transition case showed more significant improvements in the lift and drag coefficients. Moreover, the lift coefficient was improved since the actuation induced a large-scale vortex-shedding phenomenon."}}]}, "properties": [], "parentId": "165", "childIds": [], "backendDOMNodeId": 686}, {"nodeId": "266", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "While in many engineering applications, traditional large-scale physics-based models are intractable since it is required to evaluate the model to provide analysis rapidly and prediction [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "While in many engineering applications, traditional large-scale physics-based models are intractable since it is required to evaluate the model to provide analysis rapidly and prediction ["}}]}, "properties": [], "parentId": "166", "childIds": [], "backendDOMNodeId": 688}, {"nodeId": "267", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "15", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "15"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "166", "childIds": ["458"], "backendDOMNodeId": 689, "browsergym_id": "ea8cd188-cd0e-48bf"}, {"nodeId": "268", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "\u2013", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "\u2013"}}]}, "properties": [], "parentId": "166", "childIds": [], "backendDOMNodeId": 691}, {"nodeId": "269", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "17", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "17"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "166", "childIds": ["459"], "backendDOMNodeId": 692, "browsergym_id": "db641b1b-5517-4f14"}, {"nodeId": "270", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. The model reduction offers a mathematical foundation for accelerating physics-based computational models [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. The model reduction offers a mathematical foundation for accelerating physics-based computational models ["}}]}, "properties": [], "parentId": "166", "childIds": [], "backendDOMNodeId": 694}, {"nodeId": "271", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "18", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "18"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "166", "childIds": ["460"], "backendDOMNodeId": 695, "browsergym_id": "917785a8-91ea-40c2"}, {"nodeId": "272", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "\u2013", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "\u2013"}}]}, "properties": [], "parentId": "166", "childIds": [], "backendDOMNodeId": 697}, {"nodeId": "273", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "20", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "20"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "166", "childIds": ["461"], "backendDOMNodeId": 698, "browsergym_id": "f4a11ccf-f880-4f4c"}, {"nodeId": "274", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. Alternatively, the model-free approach does not rely on any underlying model description of inputs to outputs. A significant advantage of a model-free manner in flow control is that it can avoid detailed identification of high-dimensional and nonlinear flow attractors, which would even shift during the regime. Moreover, with the development of machine learning techniques, it is possible to gain massive data. The control policy must grasp the embedded evolution rules and form data-driven logic. Namely, these model-free algorithms can simulate, extend and expand human intelligence to some degree.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. Alternatively, the model-free approach does not rely on any underlying model description of inputs to outputs. A significant advantage of a model-free manner in flow control is that it can avoid detailed identification of high-dimensional and nonlinear flow attractors, which would even shift during the regime. Moreover, with the development of machine learning techniques, it is possible to gain massive data. The control policy must grasp the embedded evolution rules and form data-driven logic. Namely, these model-free algorithms can simulate, extend and expand human intelligence to some degree."}}]}, "properties": [], "parentId": "166", "childIds": [], "backendDOMNodeId": 700}, {"nodeId": "275", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "As a critical branch of artificial intelligence, deep reinforcement learning (DRL) simplifies a stochastic dynamical system by using the framework of the Markov decision process (MDP) [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "As a critical branch of artificial intelligence, deep reinforcement learning (DRL) simplifies a stochastic dynamical system by using the framework of the Markov decision process (MDP) ["}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 702}, {"nodeId": "276", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "22", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "22"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "167", "childIds": ["462"], "backendDOMNodeId": 703, "browsergym_id": "c531b755-24f4-4602"}, {"nodeId": "277", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ", ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ", "}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 705}, {"nodeId": "278", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "23", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "23"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "167", "childIds": ["463"], "backendDOMNodeId": 706, "browsergym_id": "a5e9cf87-32dd-4e6d"}, {"nodeId": "279", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. DRL algorithms can explore and adjust control policies by interacting with the environment like a child, which gets a penalty when making mistakes. In a continuous process of trial and error, the control law in DRL learns how to get sweet lollipops (high reward) and avoid penalties. Besides, DRL utilizes the artificial neural network(ANN) as a function approximator [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. DRL algorithms can explore and adjust control policies by interacting with the environment like a child, which gets a penalty when making mistakes. In a continuous process of trial and error, the control law in DRL learns how to get sweet lollipops (high reward) and avoid penalties. Besides, DRL utilizes the artificial neural network(ANN) as a function approximator ["}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 708}, {"nodeId": "280", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "24", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "24"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "167", "childIds": ["464"], "backendDOMNodeId": 709, "browsergym_id": "8868f3c1-2ec3-40c6"}, {"nodeId": "281", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. Based on the such setting, the DRL is embedded as a state representation technology, which makes it possible to deal with high-dimensional complex problems, like Go, StarCraft, Robotics [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. Based on the such setting, the DRL is embedded as a state representation technology, which makes it possible to deal with high-dimensional complex problems, like Go, StarCraft, Robotics ["}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 711}, {"nodeId": "282", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "21", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "21"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "167", "childIds": ["465"], "backendDOMNodeId": 712, "browsergym_id": "9bb84be3-4883-452f"}, {"nodeId": "283", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ", ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ", "}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 714}, {"nodeId": "284", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "25", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "25"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "167", "childIds": ["466"], "backendDOMNodeId": 715, "browsergym_id": "e3803e19-8f61-4d67"}, {"nodeId": "285", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "\u2013", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "\u2013"}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 717}, {"nodeId": "286", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "27", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "27"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "167", "childIds": ["467"], "backendDOMNodeId": 718, "browsergym_id": "3349985c-f5c9-429f"}, {"nodeId": "287", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. As shown in ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. As shown in "}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 720}, {"nodeId": "288", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "Figure 2", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "Figure 2"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "167", "childIds": ["468"], "backendDOMNodeId": 721, "browsergym_id": "2a720b3d-9986-4507"}, {"nodeId": "289", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ", Vinyals et al. [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ", Vinyals et al. ["}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 723}, {"nodeId": "290", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "21", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "21"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "167", "childIds": ["469"], "backendDOMNodeId": 724, "browsergym_id": "b8ef61e4-2194-4485"}, {"nodeId": "291", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "] adopts a multi-agent reinforcement learning algorithm to train an agent named AlphaStar, in the full game of StarCraft II, through a series of online games against a human player. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players. Similarly, DRL has highlighted its strong potential in fluid dynamics applications, including drag reduction, collective swimming, and flight control. Many excellent pioneers have reviewed the work related to the application of reinforcement learning in active flow control [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "] adopts a multi-agent reinforcement learning algorithm to train an agent named AlphaStar, in the full game of StarCraft II, through a series of online games against a human player. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players. Similarly, DRL has highlighted its strong potential in fluid dynamics applications, including drag reduction, collective swimming, and flight control. Many excellent pioneers have reviewed the work related to the application of reinforcement learning in active flow control ["}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 726}, {"nodeId": "292", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "28", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "28"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "167", "childIds": ["470"], "backendDOMNodeId": 727, "browsergym_id": "e0a8ce89-d012-4030"}, {"nodeId": "293", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "\u2013", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "\u2013"}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 729}, {"nodeId": "294", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "32", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "32"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "167", "childIds": ["471"], "backendDOMNodeId": 730, "browsergym_id": "7a6266b3-cc7b-4110"}, {"nodeId": "295", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. The present paper will further review the latest developments and demonstrate some challenges to constructing robust and effective DRL-based active flow policies.", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. The present paper will further review the latest developments and demonstrate some challenges to constructing robust and effective DRL-based active flow policies."}}]}, "properties": [], "parentId": "167", "childIds": [], "backendDOMNodeId": 732}, {"nodeId": "296", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "FIGURE 2", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "FIGURE 2"}}]}, "properties": [], "parentId": "169", "childIds": [], "backendDOMNodeId": 735}, {"nodeId": "297", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "www.frontiersin.org", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": 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"value": "What needs to be mentioned is that there are many other algorithms still active on the stage that achieve great performance and have more potentials to exploit as well, such as a gradient-enriched machine learning control [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "What needs to be mentioned is that there are many other algorithms still active on the stage that achieve great performance and have more potentials to exploit as well, such as a gradient-enriched machine learning control ["}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 749}, {"nodeId": "300", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "33", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": 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In some work, reinforcement learning has also been compared with some algorithms, such as Bayesian optimization [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. In some work, reinforcement learning has also been compared with some algorithms, such as Bayesian optimization ["}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 761}, {"nodeId": "308", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "37", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "37"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "173", "childIds": ["481"], "backendDOMNodeId": 762, "browsergym_id": "bf901ffc-2e71-46c2"}, {"nodeId": "309", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "], genetic programming [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "], genetic programming ["}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 764}, {"nodeId": "310", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "38", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "38"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "173", "childIds": ["482"], "backendDOMNodeId": 765, "browsergym_id": "c4458832-c140-4c17"}, {"nodeId": "311", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ", ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ", "}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 767}, {"nodeId": "312", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "39", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "39"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "173", "childIds": ["483"], "backendDOMNodeId": 768, "browsergym_id": "2a42f0db-e187-4da1"}, {"nodeId": "313", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "], Lipschitz global optimization [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "], Lipschitz global optimization ["}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 770}, {"nodeId": "314", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "39", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "39"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "173", "childIds": ["484"], "backendDOMNodeId": 771, "browsergym_id": "38aeb0c2-1977-469d"}, {"nodeId": "315", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "], etc. In particular, genetic programming algorithms, closely related to reinforcement learning, can achieve optimal decisions under the condition of an unknown model as well. Although the randomness of exploration brings low efficiency, evolutionary algorithms such as genetic programming are very popular in some problems like multi-objective optimization and global optimization [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "], etc. In particular, genetic programming algorithms, closely related to reinforcement learning, can achieve optimal decisions under the condition of an unknown model as well. Although the randomness of exploration brings low efficiency, evolutionary algorithms such as genetic programming are very popular in some problems like multi-objective optimization and global optimization ["}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 773}, {"nodeId": "316", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "40", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "40"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "173", "childIds": ["485"], "backendDOMNodeId": 774, "browsergym_id": "57490862-3b67-4af7"}, {"nodeId": "317", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ", ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ", "}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 776}, {"nodeId": "318", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "41", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "41"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "173", "childIds": ["486"], "backendDOMNodeId": 777, "browsergym_id": "7ae5affa-f843-4af0"}, {"nodeId": "319", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. Not only in the field of flow control but algorithms combining evolutionary algorithms and reinforcement learning have also always been expected [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. Not only in the field of flow control but algorithms combining evolutionary algorithms and reinforcement learning have also always been expected ["}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 779}, {"nodeId": "320", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "42", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "42"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "173", "childIds": ["487"], "backendDOMNodeId": 780, "browsergym_id": "af84154b-57a4-40ef"}, {"nodeId": "321", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ", ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ", "}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 782}, {"nodeId": "322", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "43", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "43"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "173", "childIds": ["488"], "backendDOMNodeId": 783, "browsergym_id": "626baf9e-886a-446b"}, {"nodeId": "323", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. For brevity, detailed comparison and discussion about the above model-free algorithms are not considered in this review, readers can refer to these papers [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. For brevity, detailed comparison and discussion about the above model-free algorithms are not considered in this review, readers can refer to these papers ["}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 785}, {"nodeId": "324", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "29", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "29"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "173", "childIds": ["489"], "backendDOMNodeId": 786, "browsergym_id": "e902cf7a-653e-4669"}, {"nodeId": "325", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ", ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ", "}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 788}, {"nodeId": "326", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "39", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "39"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "173", "childIds": ["490"], "backendDOMNodeId": 789, "browsergym_id": "6df7040d-296f-4ba5"}, {"nodeId": "327", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "].", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]."}}]}, "properties": [], "parentId": "173", "childIds": [], "backendDOMNodeId": 791}, {"nodeId": "328", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "The rest of this review is organized as follows: Section ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "The rest of this review is organized as follows: Section "}}]}, "properties": [], "parentId": "174", "childIds": [], "backendDOMNodeId": 793}, {"nodeId": "329", "ignored": false, "role": {"type": "role", "value": "emphasis"}, "chromeRole": {"type": "internalRole", "value": 81}, "name": {"type": "computedString", "value": "", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "attribute", "attribute": "title"}]}, "properties": [], "parentId": "174", "childIds": ["491"], "backendDOMNodeId": 794, "browsergym_id": "bd54bc50-e691-4556"}, {"nodeId": "330", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": " presents some basic concepts and algorithms of DRL. Section ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": " presents some basic concepts and algorithms of DRL. Section "}}]}, "properties": [], "parentId": "174", "childIds": [], "backendDOMNodeId": 796}, {"nodeId": "331", "ignored": false, "role": {"type": "role", "value": "emphasis"}, "chromeRole": {"type": "internalRole", "value": 81}, "name": {"type": "computedString", "value": "", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "attribute", "attribute": "title"}]}, "properties": [], "parentId": "174", "childIds": ["492"], "backendDOMNodeId": 797, "browsergym_id": "c30dc16e-bd71-43dc"}, {"nodeId": "332", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": " offers the application of DRL on fluids problems, and Section ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": " offers the application of DRL on fluids problems, and Section "}}]}, "properties": [], "parentId": "174", "childIds": [], "backendDOMNodeId": 799}, {"nodeId": "333", "ignored": false, "role": {"type": "role", "value": "emphasis"}, "chromeRole": {"type": "internalRole", "value": 81}, "name": {"type": "computedString", "value": "", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "attribute", "attribute": "title"}]}, "properties": [], "parentId": "174", "childIds": ["493"], "backendDOMNodeId": 800, "browsergym_id": "391c28d0-c0a6-4a67"}, {"nodeId": "334", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": " shows some innovations and solutions to fluids problems to make DRL-based active flow control more effective. Finally, a summary and potential directions of DRL-based active flow control are drawn in Section ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": " shows some innovations and solutions to fluids problems to make DRL-based active flow control more effective. Finally, a summary and potential directions of DRL-based active flow control are drawn in Section "}}]}, "properties": [], "parentId": "174", "childIds": [], "backendDOMNodeId": 802}, {"nodeId": "335", "ignored": false, "role": {"type": "role", "value": "emphasis"}, "chromeRole": {"type": "internalRole", "value": 81}, "name": {"type": "computedString", "value": "", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "attribute", "attribute": "title"}]}, "properties": [], "parentId": "174", "childIds": ["494"], "backendDOMNodeId": 803, "browsergym_id": "67583852-78a3-4b3f"}, {"nodeId": "336", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ".", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "."}}]}, "properties": [], "parentId": "174", "childIds": [], "backendDOMNodeId": 805}, {"nodeId": "337", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "Deep Reinforcement Learning", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "Deep Reinforcement Learning"}}]}, "properties": [], "parentId": "176", "childIds": [], "backendDOMNodeId": 808}, {"nodeId": "338", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "This section introduces some basics concepts of typical reinforcement learning framework, and popular deep reinforcement learning algorithms, such as proximal policy optimization (PPO) [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "This section introduces some basics concepts of typical reinforcement learning framework, and popular deep reinforcement learning algorithms, such as proximal policy optimization (PPO) ["}}]}, "properties": [], "parentId": "177", "childIds": [], "backendDOMNodeId": 810}, {"nodeId": "339", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "44", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "44"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "177", "childIds": ["495"], "backendDOMNodeId": 811, "browsergym_id": "a8237af9-9e04-4c15"}, {"nodeId": "340", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "] and soft actor-critic (SAC) [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "] and soft actor-critic (SAC) ["}}]}, "properties": [], "parentId": "177", "childIds": [], "backendDOMNodeId": 813}, {"nodeId": "341", "ignored": false, "role": {"type": "role", "value": "link"}, "chromeRole": {"type": "internalRole", "value": 110}, "name": {"type": "computedString", "value": "45", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "contents", "value": {"type": "computedString", "value": "45"}}, {"type": "attribute", "attribute": "title", "superseded": true}]}, "properties": [{"name": "focusable", "value": {"type": "booleanOrUndefined", "value": true}}], "parentId": "177", "childIds": ["496"], "backendDOMNodeId": 814, "browsergym_id": "c038ca91-d8e9-4561"}, {"nodeId": "342", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": "]. First, the general terms and concepts are presented in Section ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": "]. First, the general terms and concepts are presented in Section "}}]}, "properties": [], "parentId": "177", "childIds": [], "backendDOMNodeId": 816}, {"nodeId": "343", "ignored": false, "role": {"type": "role", "value": "emphasis"}, "chromeRole": {"type": "internalRole", "value": 81}, "name": {"type": "computedString", "value": "", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "attribute", "attribute": "title"}]}, "properties": [], "parentId": "177", "childIds": ["497"], "backendDOMNodeId": 817, "browsergym_id": "dd764781-091f-4810"}, {"nodeId": "344", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ". The optimization methods of reinforcement learning for policies are generally divided into Section ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ". The optimization methods of reinforcement learning for policies are generally divided into Section "}}]}, "properties": [], "parentId": "177", "childIds": [], "backendDOMNodeId": 819}, {"nodeId": "345", "ignored": false, "role": {"type": "role", "value": "emphasis"}, "chromeRole": {"type": "internalRole", "value": 81}, "name": {"type": "computedString", "value": "", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "attribute", "attribute": "title"}]}, "properties": [], "parentId": "177", "childIds": ["498"], "backendDOMNodeId": 820, "browsergym_id": "bf8d3a4e-3cb8-46d6"}, {"nodeId": "346", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": " and Section ", "sources": [{"type": "contents", "value": {"type": "computedString", "value": " and Section "}}]}, "properties": [], "parentId": "177", "childIds": [], "backendDOMNodeId": 822}, {"nodeId": "347", "ignored": false, "role": {"type": "role", "value": "emphasis"}, "chromeRole": {"type": "internalRole", "value": 81}, "name": {"type": "computedString", "value": "", "sources": [{"type": "relatedElement", "attribute": "aria-labelledby"}, {"type": "attribute", "attribute": "aria-label"}, {"type": "attribute", "attribute": "title"}]}, "properties": [], "parentId": "177", "childIds": ["499"], "backendDOMNodeId": 823, "browsergym_id": "ec8925bf-e7c0-4baa"}, {"nodeId": "348", "ignored": false, "role": {"type": "internalRole", "value": "StaticText"}, "chromeRole": {"type": "internalRole", "value": 158}, "name": {"type": "computedString", "value": ". Either of the two methods has the ability to find the optimal control strategy. Still, their respective shortcomings must be addressed, like a relatively large gradient variance in policy method, etc. [", "sources": [{"type": "contents", "value": {"type": "computedString", "value": ". Either of the two methods has the ability to find the optimal control strategy. 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