Upload folder using huggingface_hub

.gitattributes

@@ -57,3 +57,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+minari_export/minari_MPC/data/dataset.json filter=lfs diff=lfs merge=lfs -text
+minari_export/minari_Rule/data/dataset.json filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,504 @@
+### PyCharm+iml template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### JetBrains+all template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### JetBrains template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+__pycache__/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### PyCharm template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### JetBrains+iml template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### macOS template
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### PyCharm+all template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+

.idea/.gitignore

@@ -0,0 +1,8 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/eDriveMORL.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="offlineRLEM" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="offlineRLEM" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/eDriveMORL.iml" filepath="$PROJECT_DIR$/.idea/eDriveMORL.iml" />
+    </modules>
+  </component>
+</project>

CLTC-P-PartI.csv

@@ -0,0 +1,676 @@
+Time,Speed
+0,0.0
+1,0.0
+2,0.0
+3,0.0
+4,0.0
+5,0.0
+6,0.0
+7,0.0
+8,0.0
+9,0.0
+10,0.0
+11,0.0
+12,0.0
+13,0.0
+14,0.0
+15,3.2
+16,5.4
+17,8.5
+18,11.5
+19,14.1
+20,16.2
+21,18.7
+22,19.9
+23,19.0
+24,20.6
+25,20.7
+26,24.1
+27,25.8
+28,27.4
+29,28.9
+30,30.1
+31,31.3
+32,31.2
+33,31.2
+34,32.3
+35,33.3
+36,33.4
+37,33.6
+38,33.4
+39,33.3
+40,33.1
+41,33.3
+42,33.5
+43,33.0
+44,32.8
+45,32.6
+46,32.7
+47,32.3
+48,31.9
+49,31.3
+50,30.8
+51,30.6
+52,29.9
+53,29.1
+54,27.0
+55,25.0
+56,22.8
+57,22.1
+58,21.3
+59,20.3
+60,21.2
+61,22.2
+62,21.6
+63,22.1
+64,23.2
+65,23.8
+66,23.2
+67,22.1
+68,21.2
+69,20.0
+70,18.3
+71,15.0
+72,10.0
+73,5.7
+74,2.1
+75,0.0
+76,0.0
+77,0.0
+78,0.0
+79,0.0
+80,0.0
+81,0.0
+82,0.0
+83,0.0
+84,0.0
+85,0.0
+86,0.0
+87,0.0
+88,0.0
+89,0.0
+90,0.0
+91,0.0
+92,0.0
+93,0.0
+94,0.0
+95,0.0
+96,0.0
+97,0.0
+98,0.0
+99,0.0
+100,0.0
+101,0.0
+102,1.4
+103,2.9
+104,4.2
+105,5.3
+106,6.4
+107,7.6
+108,8.7
+109,9.7
+110,10.9
+111,11.8
+112,12.3
+113,13.1
+114,14.7
+115,16.9
+116,18.9
+117,20.2
+118,20.7
+119,20.8
+120,20.6
+121,19.9
+122,19.3
+123,19.1
+124,19.3
+125,19.9
+126,20.9
+127,22.0
+128,23.2
+129,24.3
+130,25.5
+131,26.4
+132,26.9
+133,27.0
+134,26.8
+135,26.6
+136,26.5
+137,26.6
+138,27.0
+139,27.5
+140,27.8
+141,27.6
+142,26.6
+143,25.2
+144,23.6
+145,22.1
+146,20.6
+147,19.3
+148,18.7
+149,18.7
+150,19.1
+151,19.5
+152,19.5
+153,18.9
+154,17.7
+155,16.5
+156,15.7
+157,15.5
+158,15.5
+159,15.6
+160,16.0
+161,17.0
+162,17.9
+163,18.3
+164,18.0
+165,17.1
+166,15.4
+167,13.5
+168,12.0
+169,11.1
+170,10.6
+171,10.5
+172,10.8
+173,11.2
+174,11.4
+175,11.5
+176,11.9
+177,12.4
+178,13.0
+179,13.5
+180,13.7
+181,13.8
+182,13.7
+183,13.3
+184,12.4
+185,11.4
+186,10.6
+187,10.4
+188,10.5
+189,10.9
+190,11.6
+191,12.4
+192,12.9
+193,13.2
+194,13.4
+195,13.5
+196,13.6
+197,13.6
+198,13.5
+199,13.5
+200,13.5
+201,14.0
+202,15.2
+203,16.5
+204,17.3
+205,17.6
+206,17.6
+207,17.5
+208,17.0
+209,16.3
+210,15.5
+211,14.2
+212,12.7
+213,11.4
+214,10.7
+215,10.4
+216,10.5
+217,10.9
+218,11.6
+219,12.0
+220,12.1
+221,12.2
+222,13.0
+223,15.2
+224,18.2
+225,21.0
+226,23.1
+227,24.3
+228,24.6
+229,24.8
+230,25.2
+231,26.2
+232,27.5
+233,28.5
+234,29.0
+235,29.3
+236,29.4
+237,29.2
+238,28.7
+239,27.9
+240,26.8
+241,25.1
+242,23.2
+243,21.2
+244,18.5
+245,14.9
+246,11.6
+247,9.1
+248,7.0
+249,5.6
+250,4.8
+251,4.1
+252,3.3
+253,2.5
+254,1.5
+255,0.0
+256,0.0
+257,0.0
+258,0.0
+259,0.0
+260,0.0
+261,0.0
+262,0.0
+263,0.0
+264,0.0
+265,0.0
+266,0.0
+267,0.0
+268,0.0
+269,0.0
+270,0.0
+271,0.0
+272,0.0
+273,0.0
+274,1.1
+275,3.2
+276,5.5
+277,7.8
+278,9.3
+279,9.6
+280,10.4
+281,11.3
+282,11.4
+283,9.8
+284,9.6
+285,7.1
+286,5.5
+287,4.0
+288,0.7
+289,0.0
+290,0.0
+291,0.0
+292,0.0
+293,0.0
+294,0.0
+295,0.0
+296,0.0
+297,0.0
+298,0.0
+299,0.0
+300,0.0
+301,0.0
+302,0.0
+303,0.0
+304,0.0
+305,0.0
+306,0.0
+307,0.0
+308,0.0
+309,0.0
+310,0.0
+311,0.0
+312,0.0
+313,0.0
+314,0.0
+315,0.0
+316,0.0
+317,0.0
+318,0.0
+319,0.0
+320,0.0
+321,0.0
+322,0.0
+323,0.0
+324,0.0
+325,3.6
+326,6.2
+327,9.7
+328,11.0
+329,12.2
+330,12.5
+331,12.6
+332,12.5
+333,11.6
+334,10.3
+335,8.8
+336,8.3
+337,7.4
+338,6.6
+339,6.8
+340,8.1
+341,10.1
+342,10.6
+343,10.4
+344,10.0
+345,9.4
+346,8.6
+347,5.7
+348,4.6
+349,0.0
+350,0.0
+351,0.0
+352,0.0
+353,0.0
+354,0.0
+355,0.0
+356,0.0
+357,0.0
+358,0.0
+359,0.0
+360,0.0
+361,0.0
+362,0.0
+363,0.0
+364,0.0
+365,0.0
+366,0.0
+367,0.0
+368,0.0
+369,0.0
+370,0.0
+371,0.0
+372,0.0
+373,0.0
+374,0.0
+375,0.0
+376,0.0
+377,0.0
+378,0.0
+379,0.0
+380,0.0
+381,0.0
+382,0.0
+383,0.0
+384,0.0
+385,0.0
+386,0.0
+387,0.0
+388,0.0
+389,0.0
+390,0.0
+391,0.0
+392,0.0
+393,0.0
+394,0.0
+395,0.0
+396,0.0
+397,0.0
+398,0.0
+399,0.0
+400,0.0
+401,2.0
+402,4.0
+403,5.7
+404,7.5
+405,8.8
+406,9.2
+407,10.9
+408,11.8
+409,12.6
+410,13.5
+411,14.0
+412,14.6
+413,15.3
+414,16.2
+415,16.8
+416,17.1
+417,17.5
+418,17.5
+419,17.6
+420,17.0
+421,15.1
+422,13.3
+423,11.8
+424,10.6
+425,9.6
+426,8.6
+427,7.1
+428,5.8
+429,4.9
+430,3.9
+431,6.3
+432,4.6
+433,2.1
+434,0.0
+435,0.0
+436,0.0
+437,0.0
+438,0.0
+439,0.0
+440,0.0
+441,0.0
+442,0.0
+443,0.0
+444,0.0
+445,0.0
+446,0.0
+447,0.0
+448,0.0
+449,0.0
+450,0.0
+451,0.0
+452,0.0
+453,0.0
+454,0.0
+455,0.0
+456,0.0
+457,0.0
+458,0.0
+459,0.0
+460,0.0
+461,0.0
+462,0.0
+463,0.0
+464,0.0
+465,0.0
+466,0.0
+467,0.0
+468,0.0
+469,0.0
+470,0.0
+471,0.0
+472,0.0
+473,0.0
+474,0.0
+475,0.0
+476,0.0
+477,0.0
+478,0.0
+479,0.0
+480,0.0
+481,0.0
+482,0.0
+483,0.0
+484,0.0
+485,0.0
+486,0.0
+487,0.0
+488,0.0
+489,0.0
+490,0.0
+491,0.0
+492,0.0
+493,0.0
+494,0.0
+495,0.0
+496,0.0
+497,0.0
+498,0.0
+499,0.0
+500,0.0
+501,0.0
+502,0.0
+503,0.0
+504,0.0
+505,0.0
+506,0.0
+507,0.0
+508,0.0
+509,0.0
+510,0.0
+511,0.0
+512,0.0
+513,0.0
+514,0.0
+515,0.0
+516,0.0
+517,0.0
+518,0.0
+519,0.0
+520,5.4
+521,10.6
+522,14.5
+523,15.0
+524,16.4
+525,19.7
+526,24.8
+527,26.6
+528,26.1
+529,27.1
+530,30.2
+531,33.5
+532,34.5
+533,34.0
+534,33.5
+535,34.5
+536,36.9
+537,39.2
+538,41.5
+539,43.4
+540,44.4
+541,45.2
+542,46.1
+543,46.1
+544,45.8
+545,45.5
+546,46.1
+547,46.5
+548,47.0
+549,47.5
+550,48.1
+551,48.0
+552,46.8
+553,46.0
+554,44.5
+555,42.0
+556,39.9
+557,38.3
+558,35.5
+559,31.4
+560,28.1
+561,24.1
+562,18.1
+563,14.2
+564,10.5
+565,6.3
+566,4.2
+567,2.9
+568,1.6
+569,0.0
+570,0.0
+571,0.0
+572,0.0
+573,0.0
+574,0.0
+575,0.0
+576,0.0
+577,0.0
+578,0.0
+579,4.3
+580,6.1
+581,7.8
+582,10.8
+583,14.5
+584,15.2
+585,17.4
+586,20.9
+587,22.5
+588,22.5
+589,22.4
+590,22.4
+591,23.3
+592,23.7
+593,24.2
+594,26.9
+595,30.0
+596,33.1
+597,34.1
+598,34.4
+599,36.5
+600,37.5
+601,37.2
+602,36.7
+603,37.2
+604,37.9
+605,39.6
+606,40.0
+607,41.1
+608,41.8
+609,43.2
+610,43.8
+611,44.2
+612,43.9
+613,44.3
+614,44.7
+615,45.2
+616,44.8
+617,44.2
+618,44.2
+619,44.0
+620,42.8
+621,42.7
+622,43.2
+623,42.1
+624,41.5
+625,40.6
+626,39.7
+627,38.5
+628,37.6
+629,37.1
+630,37.1
+631,37.2
+632,37.9
+633,37.8
+634,36.7
+635,36.6
+636,37.2
+637,36.2
+638,36.6
+639,36.1
+640,35.1
+641,34.1
+642,30.9
+643,23.9
+644,20.9
+645,20.4
+646,19.9
+647,20.7
+648,22.8
+649,25.2
+650,26.5
+651,26.4
+652,25.7
+653,24.7
+654,22.6
+655,20.9
+656,20.1
+657,18.7
+658,16.8
+659,15.3
+660,14.3
+661,13.6
+662,12.9
+663,12.2
+664,11.1
+665,9.9
+666,8.1
+667,5.2
+668,2.1
+669,0.0
+670,0.0
+671,0.0
+672,0.0
+673,0.0
+674,0.0

datasets/fcev-mpc-v1.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb86b23ecebfe08d3b4c6bb8b188110f3e2c3c2f5dff775c1c7c006ae75679c4
+size 222123046

datasets/fcev-rule-v1.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c92a7991777ca7312b7aff8980dfe64477323f393d1127bf537d58085a679dd2
+size 222119480

fcev.py

@@ -0,0 +1,494 @@
+import os
+import random
+import numpy as np
+import pandas as pd
+import gymnasium as gym
+from gymnasium.spaces import Box
+from scipy.interpolate import interp1d
+from dataclasses import dataclass, field
+
+def load_drive_cycle(path_or_dir):
+    """Load a drive cycle from a CSV file or a directory containing CSV files.
+
+    Args:
+        path_or_dir (str): Path to a CSV file or a directory containing CSVs.
+
+    Returns:
+        dict: Dictionary with 'Time' and 'Speed' in SI units (seconds, m/s).
+
+    Raises:
+        ValueError: If no CSV file is found or required columns are missing.
+    """
+    if os.path.isfile(path_or_dir) and path_or_dir.endswith('.csv'):
+        csv_path = path_or_dir
+    elif os.path.isdir(path_or_dir):
+        # 遍历目录及子目录下所有csv文件
+        csv_files = []
+        for root, _, files in os.walk(path_or_dir):
+            for f in files:
+                if f.endswith('.csv'):
+                    csv_files.append(os.path.join(root, f))
+        if not csv_files:
+            raise ValueError(f"No CSV file found in directory: {path_or_dir}")
+        csv_path = random.choice(csv_files)
+        print(f"Randomly selected CSV drive cycle: {csv_path}")
+    else:
+        raise ValueError(f"Invalid path: {path_or_dir}")
+
+    df = pd.read_csv(csv_path)
+
+    if 'Time' not in df.columns or 'Speed' not in df.columns:
+        raise ValueError("CSV must contain 'Time' and 'Speed' columns")
+
+    # Ensure time starts at zero
+    time = df['Time'].to_numpy()
+    time = time - time[0]
+
+    return {
+        'Time': time,
+        'Speed': df['Speed'].to_numpy() / 3.6 # Convert km/h to m/s
+    }
+
+
+def fcev_ext(u, w, veh, fd, fc, batt):
+    """
+    External dynamic model for a fuel cell electric vehicle (FCEV) primarily powered by a fuel cell.
+    Supports the main cooling loop with a three-way valve split control structure.
+
+    Inputs:
+        u[0]: Fuel cell output ratio (range: 0 to 1)
+        u[1]: Total cooling level (normalized, range: 0.2 to 1.0)
+        u[2]: Split ratio to the fuel cell system (FCS) (range: 0 to 1)
+        w[0]: Vehicle speed (m/s)
+        w[1]: Vehicle acceleration (m/s²)
+
+    Returns:
+        intVar: List of intermediate variables
+        unfeas: Infeasibility flag
+    """
+
+    # === Input Processing ===
+    v = w[0]  # vehicle speed
+    a = w[1]  # vehicle acceleration
+    fc_ratio = u[0]  # 燃料电池功率输出比例
+    cooling_level = u[1]  # 冷却强度 [0.2, 1.0]
+    split_ratio = u[2]  # 三通阀分流比例 α
+
+    # === Wheel Dynamics ===
+    wheel_spd = v / veh.wh_radius
+    wheel_acc = a / veh.wh_radius
+
+    rolling = veh.mass * veh.gravity * (veh.first_rrc + veh.second_rrc * v)
+    aero = veh.aero_coeff * v ** 2
+    inertia = veh.mass * a
+    veh_force = rolling + aero + inertia
+
+    wheel_trq = veh_force * veh.wh_radius + veh.axle_loss * (wheel_spd != 0)
+
+    # === Final Drive ===
+    fd_spd = fd.spdRatio * wheel_spd
+    fd_trq = wheel_trq / fd.spdRatio + fd.loss * np.sign(wheel_trq)
+
+    # === Simple Motor Dynamics ===
+    shaft_spd = fd_spd
+    shaft_trq = fd_trq
+    mot_pwr = shaft_spd * shaft_trq
+
+    # === Fuel Cell Ideal Output ===
+    fc_ratio = np.clip(fc_ratio, 0, 1)
+    fc_pwr = fc_ratio * fc.maxPwr
+    batt_pwr = mot_pwr - fc_pwr
+
+    # === Fuel Cell Efficiency & Hydrogen Usage ===
+    fc_eff = fc.effMap(fc_pwr)
+    fc_eff = np.maximum(fc_eff, np.finfo(float).eps)
+    h2_rate = fc.h2Map(fc_pwr)
+    # print(f"H2 Rate: {h2_rate}, || Fuel Cell Power: {fc_pwr}")
+
+    # === Cooling System ===
+    fc.maxCoolFlow = 0.2  # 假设的最大冷却流速 kg/s
+    m_dot_total = cooling_level * fc.maxCoolFlow
+
+    m_dot_fc = split_ratio * m_dot_total
+    m_dot_batt = (1 - split_ratio) * m_dot_total
+
+    # === Feasibility Check ===
+    fc_unfeas = (fc_pwr > fc.maxPwr) | (fc_pwr < fc.minPwr)
+    batt_unfeas = (batt_pwr > batt.maxPwr) | (batt_pwr < batt.minPwr)
+    unfeas = fc_unfeas | batt_unfeas
+
+    # === Output Intermediate Variables ===
+    intVar = [
+        batt_pwr,  # 1
+        fc_pwr,  # 2
+        h2_rate,  # 3
+        np.ones_like(fc_pwr) * shaft_spd,  # 4
+        m_dot_fc,  # 5
+        m_dot_batt  # 6
+    ]
+
+    return intVar, unfeas
+
+
+def fcev_int(x, w, u, intVar, batt, fc):
+    """
+     Internal state update function for a fuel cell electric vehicle (FCEV),
+     including thermal and electrical models for both battery and fuel cell.
+
+     Args:
+         x (list[float]): Current state variables:
+             x[0]: State of Charge (SOC)
+             x[1]: Fuel cell temperature (T_fc)
+             x[2]: Battery core temperature (T_core)
+             x[3]: Battery surface temperature (T_surf)
+         w (Union[list, dict]): External inputs (e.g., vehicle speed/acc) and previous controls if available.
+         u (list[float]): Control inputs:
+             u[0]: Fuel cell output ratio (0 to 1)
+             u[1]: Normalized total cooling level (0.2 to 1.0)
+             u[2]: Coolant split ratio to fuel cell (0 to 1)
+         intVar (list[float]): Intermediate variables:
+             intVar[0]: Battery power
+             intVar[1]: Fuel cell power
+             intVar[2]: Hydrogen consumption rate
+             intVar[4]: Coolant flow rate through fuel cell
+             intVar[5]: Coolant flow rate through battery
+         batt: Battery model object (with methods like `ocv()`, `chrgRes()`, `dischrgRes()`, etc.)
+         fc: Fuel cell model object (with methods like `effMap()` and `heatCap`)
+
+     Returns:
+         tuple:
+             x_new (list[float]): Updated state vector [SOC, T_fc, T_core, T_surf]
+             stageCost (float): Calculated cost including hydrogen consumption and penalties
+             unfeas (bool): Feasibility flag (True if thermal or electrical limits exceeded)
+             H2_rate (float): Hydrogen consumption rate
+             dT_core (float): Battery core temperature change rate
+             dT_fc (float): Fuel cell temperature change rate
+    """
+    # === Extract state variables ===
+    SOC = x[0]
+    T_fc = x[1]
+    T_core = x[2]
+    T_surf = x[3]
+
+    # === Extract intermediate variables ===
+    battPwr = intVar[0]
+    fcPwr = intVar[1]
+    H2_rate = intVar[2]
+    m_dot_fc = intVar[4]
+    m_dot_batt = intVar[5]
+
+    # === Constants ===
+    dt = 1.0                  # Time step (s)
+    T_amb = 20.0              # Ambient temperature (°C)
+    cp = 4180.0               # Specific heat capacity of coolant (J/kg°C)
+
+    # === Battery electrical model ===
+    battPwr_adj = np.where(battPwr > 0, battPwr / 0.95, battPwr * 0.95)
+    Rbat = np.where(battPwr > 0, batt.dischrgRes(SOC), batt.chrgRes(SOC))
+    Vbat = batt.ocv(SOC)
+
+    sqrt_term = Vbat ** 2 - 4 * Rbat * battPwr_adj
+    sqrt_term = np.maximum(sqrt_term, 0)
+    Ibatt = (Vbat - np.sqrt(sqrt_term)) / (2 * Rbat)
+    Ibatt = np.real(Ibatt)
+
+    # === SOC update ===
+    SOC_new = SOC - Ibatt / (batt.maxCap * 3600) * dt
+    battUnfeas = (Ibatt > batt.maxCurr(SOC)) | (Ibatt < batt.minCurr(SOC))
+
+    # === Fuel Cell Thermal Model ===
+    eta_fc = np.maximum(fc.effMap(fcPwr), np.finfo(float).eps)
+    Qgen_fc = (1 - eta_fc) * fcPwr
+    Qcool_fc = m_dot_fc * cp * (T_fc - T_amb)
+    dT_fc = (Qgen_fc - Qcool_fc) / fc.heatCap
+    T_fc_new = T_fc + dt * dT_fc
+
+    # === Battery thermal model ===
+    Qgen_batt = Ibatt ** 2 * Rbat
+    Rth_in = 0.2
+    Ccore = 3000.0
+    Csurf = 2000.0
+    Qcool_batt = m_dot_batt * cp * (T_surf - T_amb)
+
+    dT_core = (Qgen_batt - (T_core - T_surf) / Rth_in) / Ccore
+    dT_surf = ((T_core - T_surf) / Rth_in - Qcool_batt) / Csurf
+
+    T_core_new = T_core + dt * dT_core
+    T_surf_new = T_surf + dt * dT_surf
+
+    # === Stage cost calculation ===
+    stageCost = H2_rate
+
+    # Add cooling system power penalty (quadratic)
+    m_dot_total = m_dot_fc + m_dot_batt
+    stageCost += 1e-3 * m_dot_total ** 2
+
+    # Add control smoothness penalty (Δu²)
+    if isinstance(w, dict) and "u_prev" in w:
+        u_prev = w["u_prev"][0]  # 之前的fcRatio
+        fcRatio = u[0]
+        delta_u = fcRatio - u_prev
+        stageCost += 1e-4 * delta_u ** 2
+
+    # === Temperature feasibility check (hard constraint) ===
+    T_ref_fc = 60.0
+    T_th_fc = 80.0
+    k_fc = 8.0
+
+    # --- Battery degradation penalty ---
+    T_ref_batt = 35.0
+    T_th_batt = 45.0
+    k_batt = 6.0
+    if T_core >= 100 or T_fc >= 100:
+        T_core = np.clip(T_core, 0, 100)  # 或 120 上限
+        T_fc = np.clip(T_fc, 0, 100)
+        TempUnfeas = True
+    else:
+        TempUnfeas = False
+
+    # === Lifetime degradation penalties (soft constraint) ===
+
+    # Fuel cell degradation penalty
+    fc_life_penalty = np.where(
+        T_fc > T_th_fc,
+        1e-2 * np.exp((T_fc - T_ref_fc) / k_fc),
+        0.0
+    )
+
+    # Battery degradation penalty
+    batt_life_penalty = np.where(
+        T_core > T_th_batt,
+        1e-2 * np.exp((T_core - T_ref_batt) / k_batt),
+        0.0
+    )
+
+
+    stageCost += fc_life_penalty + batt_life_penalty
+
+    # === Output new states ===
+    x_new = [
+        SOC_new,
+        T_fc_new,
+        T_core_new,
+        T_surf_new
+    ]
+
+    # Combine feasibility flags
+    unfeas = battUnfeas or TempUnfeas
+
+    if unfeas:
+        print(battUnfeas, f" {SOC} || {T_core} || {T_surf} || {T_fc} || f{Ibatt} || f{batt.maxCurr(SOC)} || f{batt.minCurr(SOC)}")
+
+    return x_new, stageCost, unfeas, H2_rate, dT_core, dT_fc
+
+
+@dataclass
+class Vehicle:
+    mass: float = 1920  # kg
+    gravity: float = 9.81
+    wh_radius: float = 0.32  # m
+    first_rrc: float = 0.009
+    second_rrc: float = 0.001
+    aero_coeff: float = field(init=False)
+    axle_loss: float = 100
+
+    def __post_init__(self):
+        Cd = 0.29
+        A = 2.3
+        rho = 1.225
+        self.aero_coeff = 0.5 * Cd * A * rho
+
+
+@dataclass
+class FinalDrive:
+    spdRatio: float = 9.0
+    loss: float = 50
+
+
+@dataclass
+class FuelCell:
+    minPwr: float = 0
+    maxPwr: float = 114000
+    pwrBrk: np.ndarray = field(default_factory=lambda: np.linspace(0, 114000, 20))
+    etaBrk: np.ndarray = field(default_factory=lambda: np.array([
+        0.0, 0.25, 0.4, 0.48, 0.53, 0.56, 0.59, 0.6,
+        0.605, 0.61, 0.6, 0.59, 0.58, 0.57, 0.56, 0.54,
+        0.52, 0.5, 0.48, 0.46
+    ]))
+    thermEff: float = 0.4
+    coolingCoeff: float = 40
+    heatCap: float = 20000
+    effMap: callable = field(init=False)
+    h2Map: callable = field(init=False)
+
+    def __post_init__(self):
+        eff_interp = interp1d(self.pwrBrk, self.etaBrk, kind='linear', fill_value='extrapolate')
+        self.effMap = lambda P: np.minimum(1, np.maximum(eff_interp(P), np.finfo(float).eps))
+
+        LHV_H2 = 33.3e6  # J/kg
+        self.h2Map = lambda P: 1e3 * P / np.maximum(self.effMap(P) * LHV_H2, np.finfo(float).eps)
+
+
+@dataclass
+class Battery:
+    maxCap: float = 10  # Ah
+    minPwr: float = -20000  # W
+    maxPwr: float = 25000  # W
+    socBrk: np.ndarray = field(default_factory=lambda: np.linspace(0, 1, 11))
+    ocvData: np.ndarray = field(init=False)
+    chrgResData: np.ndarray = field(init=False)
+    dischrgResData: np.ndarray = field(init=False)
+    minCurrData: np.ndarray = field(init=False)
+    maxCurrData: np.ndarray = field(init=False)
+    ocv: callable = field(init=False)
+    chrgRes: callable = field(init=False)
+    dischrgRes: callable = field(init=False)
+    minCurr: callable = field(init=False)
+    maxCurr: callable = field(init=False)
+    coolingCoeff: float = 10
+    heatCap: float = 6000
+
+    def __post_init__(self):
+        self.ocvData = 320 + 20 * self.socBrk
+        self.chrgResData = 0.3 - 0.1 * self.socBrk
+        self.dischrgResData = 0.2 + 0.05 * (1 - self.socBrk)
+        self.minCurrData = -120 + np.zeros_like(self.socBrk)
+        self.maxCurrData = 180 + np.zeros_like(self.socBrk)
+
+        self.ocv = interp1d(self.socBrk, self.ocvData, kind='linear', fill_value='extrapolate')
+        self.chrgRes = interp1d(self.socBrk, self.chrgResData, kind='linear', fill_value='extrapolate')
+        self.dischrgRes = interp1d(self.socBrk, self.dischrgResData, kind='linear', fill_value='extrapolate')
+        self.minCurr = interp1d(self.socBrk, self.minCurrData, kind='linear', fill_value='extrapolate')
+        self.maxCurr = interp1d(self.socBrk, self.maxCurrData, kind='linear', fill_value='extrapolate')
+
+
+def fcev_fcdom_data():
+    veh = Vehicle()
+    fd = FinalDrive()
+    fc = FuelCell()
+    batt = Battery()
+    return veh, fd, fc, batt
+
+def reward_function(stage_cost, speed, beta=0.01, c=10):
+    if stage_cost == 0 and speed == 0:
+        return 0.0
+    return 1 / (1 + np.exp(beta * (stage_cost - c)))
+
+class FCEVEnv(gym.Env):
+    """
+    Custom OpenAI Gym environment for a fuel cell electric vehicle (FCEV) with
+    thermal-electric hybrid modeling. The environment simulates internal vehicle
+    dynamics and evaluates control actions based on energy efficiency and constraints.
+
+    Observations:
+        [vehicle_speed, acceleration, SOC, T_fc, T_core, T_surf]
+
+    Actions:
+        [fuel_cell_output_ratio (0~1), cooling_level (0.2~1.0), coolant_split_ratio (0~1)]
+
+    Reward:
+        Based on custom reward function applied to stage cost.
+    """
+    def __init__(self, drive_cycle):
+        super(FCEVEnv, self).__init__()
+        self.dt = 1.0
+
+        # Load vehicle, powertrain, battery, and fuel cell parameters
+        self.veh, self.fd, self.fc, self.batt = fcev_fcdom_data()
+
+        # Load and interpolate driving cycle to match simulation timestep
+        self.speed_profile = np.interp(
+            np.arange(0, drive_cycle['Time'][-1] + 1),
+            drive_cycle['Time'] - drive_cycle['Time'][0],
+            drive_cycle['Speed']
+        )
+        self.steps = len(self.speed_profile)
+        self.step_count = 0
+
+        # Obs Space：[SOC, T_fc, T_core, T_surf, speed, acceleration]
+        self.observation_space = Box(low=np.array([-np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf]),
+                                            high=np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf]),
+                                            dtype=np.float32)
+
+        # Act Space：[fc_ratio, cooling_level, split_ratio]
+        self.action_space = Box(low=np.array([0, 0, 0]),
+                                       high=np.array([1.0, 1.0, 1.0]),
+                                       dtype=np.float32)
+
+        self.reset()
+
+    def reset(self):
+        """Reset the simulation to initial state."""
+        self.state = [0.5, 25, 25, 25]  # [SOC, T_fc, T_core, T_surf]
+        self.step_count = 0
+        return self._get_obs(), None
+
+    def _get_obs(self):
+        """Get the current observation including vehicle dynamics and system state."""
+        speed = self.speed_profile[self.step_count] if self.step_count < self.steps else self.speed_profile[self.step_count - 1]
+        if self.step_count + 1 < self.steps:
+            next_speed = self.speed_profile[self.step_count + 1]
+        else:
+            next_speed = self.speed_profile[self.step_count]
+        acc = (next_speed - speed) / self.dt
+        return np.array([speed, acc] + self.state, dtype=np.float32)
+
+    def step(self, action):
+        """
+        Apply a control action and update the environment state.
+
+        Args:
+            action (list or ndarray): Action input [fc_ratio, cooling_level, split_ratio]
+
+        Returns:
+            tuple: (observation, reward, done, unfeasible_flag, info_dict)
+        """
+        u = [float(a) for a in action]
+        speed = self.speed_profile[self.step_count]
+        if self.step_count + 1 < self.steps:
+            next_speed = self.speed_profile[self.step_count + 1]
+        else:
+            next_speed = self.speed_profile[self.step_count]
+
+        acc = (next_speed - speed) / self.dt
+
+        w = [speed, acc]
+
+        # 执行外部动力学
+        intVar, _ = fcev_ext(u, w, self.veh, self.fd, self.fc, self.batt)
+
+        # 内部状态更新
+        x_new, stageCost, unfeas, h2_rate, dT_core, dT_fc = fcev_int(self.state, w, u, intVar, self.batt, self.fc)
+
+        self.state = x_new
+        self.step_count += 1
+        done = self.step_count >= self.steps - 1 or unfeas
+        # print(f"{self.step_count >= self.steps} or {unfeas}")
+        obs = self._get_obs()
+        reward = reward_function(stageCost, speed) if not unfeas else -99
+
+
+        return obs, reward, done, unfeas, {"H2_rate": h2_rate * self.dt, "dT_core": dT_core * self.dt, "dT_fc": dT_fc * self.dt}
+
+    def render(self, mode='human'):
+        """Render the current step's state to console."""
+        print(f"Step {self.step_count}, State: {self.state}")
+
+    def get_logs(self):
+        """Return final state and step count for logging."""
+        return {
+            "final_state": self.state,
+            "step_count": self.step_count
+        }
+
+
+if __name__ == '__main__':
+    veh, fd, fc, batt = fcev_fcdom_data()
+
+    # Demo
+    P_test = np.array([0, 20000, 60000, 110000])
+    eff = fc.effMap(P_test)
+    h2 = fc.h2Map(P_test)
+
+    print("H2 Efficiency:", eff)
+    print("Hydrogen Consumption (g/s):", h2)

minari_export/minari_MPC/data/dataset.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c254a21cdea0faab1838e21ce9986839abc978d67a85f78ec5af712161bf60c
+size 1798380054

minari_export/minari_MPC/dataset_info.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "minari_MPC",
+  "env_id": "FCEV-SimulatedEnv-v0",
+  "total_episodes": 9197,
+  "total_steps": 5.194267E+6,
+  "observation_dimension": 6,
+  "action_dimension": 3,
+  "reward_range": [
+    1.0000000000000003E-5,
+    513.01058735562515
+  ],
+  "creation_date": "2025-04-15 22:24:19",
+  "author": "Your Name or Lab",
+  "strategy_type": "MPC"
+}

minari_export/minari_Rule/data/dataset.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88c821505a70c2518b6cb4251498953e5d631aeb65079e5cf3540384a928a712
+size 1946797994

minari_export/minari_Rule/dataset_info.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "minari_Rule",
+  "env_id": "FCEV-SimulatedEnv-v0",
+  "total_episodes": 9197,
+  "total_steps": 5.194267E+6,
+  "observation_dimension": 6,
+  "action_dimension": 3,
+  "reward_range": [
+    1.8567978538054825E-6,
+    2002.4124479044558
+  ],
+  "creation_date": "2025-04-15 22:30:42",
+  "author": "Your Name or Lab",
+  "strategy_type": "Rule"
+}

register_minari_dataset.py

@@ -0,0 +1,140 @@
+import os
+import json
+import numpy as np
+import gymnasium as gym
+from gymnasium.spaces import Box
+import minari
+from minari.data_collector import EpisodeBuffer
+from minari import create_dataset_from_buffers
+from gymnasium.envs.registration import EnvSpec
+from numpy import dtype
+from fcev import FCEVEnv, load_drive_cycle
+
+def reward_function(stage_cost, state, beta=0.01, c=10):
+    """Custom reward function based on stage cost and system state.
+
+    Args:
+        stage_cost (float): Original stage cost.
+        state (np.ndarray): Observation state.
+        beta (float): Slope parameter for logistic transformation.
+        c (float): Cost offset threshold.
+
+    Returns:
+        float: Transformed reward value.
+    """
+    if stage_cost == 0 and state[0] == 0:
+        return 0.0
+    return 1 / (1 + np.exp(beta * (stage_cost - c)))
+
+
+def load_matlab_json_data(json_path):
+    with open(json_path, 'r') as f:
+        data = json.load(f)
+
+    episodes = dict()
+
+    for t in data:
+        eid = t.get("episode_id", 0)
+        if eid not in episodes:
+            episodes[eid] = {
+                "observations": [],
+                "actions": [],
+                "rewards": [],
+                "next_observations": [],
+                "terminations": [],
+            }
+        episodes[eid]["observations"].append(t["observation"])
+        episodes[eid]["actions"].append(t["action"])
+        episodes[eid]["rewards"].append(reward_function(stage_cost=t["reward"],state=t["observation"]))
+        episodes[eid]["next_observations"].append(t["next_observation"])
+        episodes[eid]["terminations"].append(t["termination"])
+
+    for epi in episodes.keys():
+        episodes[epi]["observations"] = np.array(episodes[epi]["observations"], dtype=np.float32)
+        episodes[epi]["actions"] = np.array(episodes[epi]["actions"], dtype=np.float32)
+        episodes[epi]["rewards"] = np.array(episodes[epi]["rewards"], dtype=np.float32)
+        if np.isnan(episodes[epi]["rewards"]).any():
+            print("Detected NaN, in episode", eid)
+            episodes[epi]["rewards"] = np.nan_to_num(episodes[epi]["rewards"], nan=0)
+        episodes[epi]["next_observations"] = np.array(episodes[epi]["next_observations"], dtype=np.float32)
+        episodes[epi]["terminations"] = np.array(episodes[epi]["terminations"], dtype=np.bool)
+
+    return episodes
+
+
+def register_minari_dataset(folder_path, dataset_id):
+    dataset_json_path = os.path.join(folder_path, 'data', 'dataset.json')
+    info_json_path = os.path.join(folder_path, 'dataset_info.json')
+
+    # load data from json
+    episodes = load_matlab_json_data(dataset_json_path)
+
+    with open(info_json_path, 'r') as f:
+        info = json.load(f)
+
+    # example_ep = episodes[1]
+    # obs_dim = example_ep["observations"].shape[1]
+    # act_dim = example_ep["actions"].shape[1]
+    #
+    # observation_space = Box(-np.inf, np.inf, shape=(obs_dim,), dtype=np.float32)
+    # action_space = Box(0.0, 1.0, shape=(act_dim,), dtype=np.float32)
+    #
+    # class DummyEnv(gym.Env):
+    #     def __init__(self):
+    #         self.observation_space = observation_space
+    #         self.action_space = action_space
+    #         self.spec = EnvSpec(id="FCEV-SimulatedEnv-v0")
+    #
+    # env = DummyEnv()
+    env = FCEVEnv(load_drive_cycle("CLTC-P-PartI.csv"))
+
+    buffers = []
+
+    xid = 0
+
+    for eid, ep in episodes.items():
+        obs = ep["observations"]
+        next_obs = ep["next_observations"]
+        actions = ep["actions"]
+        rewards = ep["rewards"]
+        rewards = np.clip(rewards, -1e6, 1e6)
+        terminations =ep["terminations"]
+        truncations = np.zeros_like(terminations, dtype=bool)
+
+        full_obs = np.vstack([obs, next_obs[-1:]])  # 末尾补 observation
+
+        buffer = EpisodeBuffer(
+            id=xid,
+            observations=full_obs,
+            actions=actions,
+            rewards=rewards,
+            terminations=terminations,
+            truncations=truncations,
+        )
+        buffers.append(buffer)
+        xid += 1
+
+    dataset = create_dataset_from_buffers(
+        dataset_id=dataset_id,
+        env=env,
+        buffer=buffers,
+        algorithm_name=info.get("strategy_type", "unknown"),
+        author=info.get("author", "matlab-export"),
+    )
+
+    print(f"✅ Minari dataset formed：{dataset_id}")
+    return dataset
+
+
+if __name__ == "__main__":
+    base_dir = "minari_export"
+    strategies = ["mpc", "rule"]
+
+    for strat in strategies:
+        folder = os.path.join(base_dir, f"minari_{strat}")
+        dataset_id = f"fcev-{strat}-v1"
+        try:
+            minari.delete_dataset(dataset_id)
+        except:
+            pass
+        register_minari_dataset(folder, dataset_id)

requirements.txt

@@ -0,0 +1,7 @@
+wandb~=0.19.9
+d3rlpy~=2.8.1
+numpy~=2.0.2
+pandas~=2.2.3
+gymnasium~=1.0.0
+scipy~=1.13.1
+minari~=0.5.2

run.py

@@ -0,0 +1,147 @@
+import wandb
+import d3rlpy
+import argparse
+import traceback
+
+from d3rlpy.dataset import ReplayBuffer, InfiniteBuffer
+from d3rlpy.preprocessing import StandardObservationScaler
+from d3rlpy.logging import CombineAdapterFactory, FileAdapterFactory, TensorboardAdapterFactory
+from fcev import FCEVEnv, load_drive_cycle
+from d3rlpy.algos import (
+    TD3PlusBCConfig, IQLConfig, CQLConfig, BCQConfig,
+    CalQLConfig, AWACConfig, ReBRACConfig, TACRConfig,
+    PLASConfig, PRDCConfig, BEARConfig
+)
+
+from typing import Any, Optional
+from d3rlpy.logging import WanDBAdapter
+from d3rlpy.logging.logger import (
+    AlgProtocol,
+    LoggerAdapter,
+    LoggerAdapterFactory,
+    SaveProtocol,
+)
+
+# ---------- WandB Logger Factory ----------
+class GWanDBAdapterFactory(LoggerAdapterFactory):
+    r"""WandB Logger Adapter Factory class.
+
+    This class creates instances of the WandB Logger Adapter for experiment
+    tracking.
+
+    Args:
+        project (Optional[str], optional): The name of the WandB project.
+            Defaults to None.
+    """
+
+    _project: Optional[str]
+
+    def __init__(self, project: Optional[str] = None, experiment_name: Optional[str] = None,) -> None:
+        self._project = project
+
+    def create(
+        self, algo: AlgProtocol, experiment_name: str, n_steps_per_epoch: int
+    ) -> LoggerAdapter:
+        return WanDBAdapter(
+            algo=algo,
+            experiment_name=experiment_name,
+            n_steps_per_epoch=n_steps_per_epoch,
+            project=self._project,
+        )
+
+
+
+# ---------- Algorithm Config Dictionary ----------
+def get_algo_configs():
+    # Algorithm configurations with encoder and observation preprocessing settings
+    algo_configs = {
+        "TD3PlusBC": TD3PlusBCConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), observation_scaler=StandardObservationScaler()),
+        "IQL": IQLConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), observation_scaler=StandardObservationScaler()),
+        "CQL": CQLConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), observation_scaler=StandardObservationScaler()),
+        "BCQ": BCQConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), observation_scaler=StandardObservationScaler()),
+        "CalQL": CalQLConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), observation_scaler=StandardObservationScaler()),
+        "AWAC": AWACConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), observation_scaler=StandardObservationScaler()),
+        "ReBRAC": ReBRACConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2),  q_func_factory=d3rlpy.models.QRQFunctionFactory()),
+        "TACR": TACRConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), observation_scaler=StandardObservationScaler()),
+        "PLAS": PLASConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), observation_scaler=StandardObservationScaler()),
+        "PRDC": PRDCConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), observation_scaler=StandardObservationScaler()),
+        "BEAR": BEARConfig(actor_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), critic_encoder_factory=d3rlpy.models.DefaultEncoderFactory(use_batch_norm=True, dropout_rate=0.2), observation_scaler=StandardObservationScaler()),
+    }
+    return algo_configs
+
+# ---------- Training Function ----------
+def train(args):
+    algo_configs = get_algo_configs()
+
+    if args.algo not in algo_configs:
+        raise ValueError(f"Unsupported algorithm: {args.algo}")
+
+    # Load dataset
+    with open(args.dataset_path, "rb") as f:
+        dataset = ReplayBuffer.load(f, InfiniteBuffer())
+
+    # Load environment for evaluation
+    env = FCEVEnv(load_drive_cycle(args.drive_cycle))
+
+    config = algo_configs[args.algo]
+    algo = config.create(device=args.device)
+
+    # Setup logger
+    logger_adapters = [
+        FileAdapterFactory(root_dir=f"d3rlpy_logs/{args.algo}"),
+        TensorboardAdapterFactory(root_dir=f"tensorboard_logs/{args.algo}")
+    ]
+    if args.wandb:
+        logger_adapters.append(GWanDBAdapterFactory(experiment_name=f"{args.algo}-run", project=args.wandb_project))
+
+    logger_adapter = CombineAdapterFactory(logger_adapters)
+
+    try:
+        print(f"\n🚀 Starting training: {args.algo}")
+
+        algo.fit(
+            dataset,
+            n_steps=args.n_steps,
+            n_steps_per_epoch=args.n_steps_per_epoch,
+            logger_adapter=logger_adapter,
+            evaluators={
+                'init_value': d3rlpy.metrics.InitialStateValueEstimationEvaluator(),
+                'soft_opc': d3rlpy.metrics.SoftOPCEvaluator(return_threshold=100),
+                'action': d3rlpy.metrics.ContinuousActionDiffEvaluator(),
+                'environment': d3rlpy.metrics.EnvironmentEvaluator(env),
+                'Advantage': d3rlpy.metrics.DiscountedSumOfAdvantageEvaluator()
+            },
+        )
+
+        print(f"\n✅ Training finished for: {args.algo}")
+
+    except Exception as e:
+        print(f"\n❌ Training failed: {args.algo}")
+        print(traceback.format_exc())
+        wandb.finish()
+
+
+# ---------- Main CLI ----------
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Offline RL training for FCEV")
+
+    parser.add_argument("--algo", type=str, default="AWAC",
+                        choices=list(get_algo_configs().keys()),
+                        help="Name of the offline RL algorithm")
+    parser.add_argument("--dataset-path", type=str, default="datasets/fcev-mpc-v1.h5",
+                        help="Path to the .h5 dataset file")
+    parser.add_argument("--drive-cycle", type=str, default="CLTC-P-PartI.csv",
+                        help="Path to the drive cycle CSV file")
+    parser.add_argument("--n-steps", type=int, default=10000,
+                        help="Total number of training steps")
+    parser.add_argument("--n-steps-per-epoch", type=int, default=100,
+                        help="Steps per epoch")
+    parser.add_argument("--device", type=str, default="cuda:0",
+                        help="Training device (e.g., 'cpu', 'cuda:0')")
+    parser.add_argument("--wandb", action="store_true",
+                        help="Enable WandB logging")
+    parser.add_argument("--wandb-project", type=str, default="fcev-offline-benchmark",
+                        help="WandB project name (used only if --wandb is enabled)")
+
+    args = parser.parse_args()
+    train(args)

train.py

@@ -0,0 +1,95 @@
+import minari
+import numpy as np
+import d3rlpy.dataset
+from d3rlpy.dataset import MDPDataset
+from fcev import FCEVEnv, load_drive_cycle
+from d3rlpy.algos import SACConfig, TD3PlusBCConfig, IQLConfig, CQLConfig, BCQConfig, CalQLConfig, AWACConfig, \
+    ReBRACConfig, TACRConfig, PLASConfig, PRDCConfig, BEARConfig, DecisionTransformerConfig, CalQL
+
+def load_minari_as_d3rlpy(name="fcev-mpc-v1", num=None):
+    """Load Minari dataset with a custom reward function.
+
+    Args:
+        name (str): Dataset name.
+        num (int, optional): Number of episodes to sample.
+        beta (float): Logistic function slope.
+        c (float): Offset for logistic transformation.
+
+    Returns:
+        MDPDataset: Dataset with custom rewards.
+    """
+    dataset = minari.load_dataset(name)
+    episodes = dataset.sample_episodes(num) if num else dataset.sample_episodes(dataset.total_episodes)
+
+    all_obs = []
+    all_actions = []
+    all_rewards = []
+    all_terminals = []
+
+    for ep in episodes:
+        obs = ep.observations[:-1]
+        actions = ep.actions
+        rewards = ep.rewards
+        terminals = ep.terminations
+
+        n = len(actions)
+        obs = obs[:n]
+        actions = actions[:n]
+        rewards = rewards[:n]
+        terminals = terminals[:n]
+
+        all_obs.append(obs)
+        all_actions.append(actions)
+        all_rewards.append(rewards)
+        all_terminals.append(terminals)
+
+    obs = np.vstack(all_obs)
+    act = np.vstack(all_actions)
+    reward = np.hstack(all_rewards)
+    terminal = np.hstack(all_terminals)
+
+    return MDPDataset(
+        observations=obs,
+        actions=act,
+        rewards=reward,
+        terminals=terminal
+    )
+
+
+# Define environment and dataset
+env_name = "fcev-mpc-v1"
+# env_name = "fcev-rule-v1"
+
+# Save dataset to disk
+dataset = load_minari_as_d3rlpy(env_name)
+
+# Reload dataset using ReplayBuffer
+dataset.dump(f"datasets/{env_name}.h5")
+with open(f"datasets/{env_name}.h5", "rb") as f:
+    dataset = d3rlpy.dataset.ReplayBuffer.load(f, d3rlpy.dataset.InfiniteBuffer())
+# dataset = d3rlpy.datasets.get_minari("fcev-mpc-v1")
+
+# Select and build algorithm
+# algo = SACConfig(compile_graph=True).create()
+# algo = TD3PlusBCConfig(compile_graph=True).create()
+algo = CQLConfig(compile_graph=True).create()
+# algo = BCQConfig(compile_graph=True).create()
+# algo = IQLConfig(compile_graph=True).create()
+# algo = CalQLConfig(compile_graph=True).create()
+# algo = DecisionTransformerConfig(compile_graph=True).create()
+
+# Setup logging
+algo.build_with_env(env=FCEVEnv(load_drive_cycle("CLTC-P-PartI.csv")))
+
+# Setup FileAdapterFactory and TensorboardAdapterFactory
+logger_adapter = d3rlpy.logging.CombineAdapterFactory([
+    d3rlpy.logging.FileAdapterFactory(root_dir="d3rlpy_logs"),
+    d3rlpy.logging.TensorboardAdapterFactory(root_dir="tensorboard_logs"),
+    d3rlpy.logging.WanDBAdapterFactory()
+])
+
+# Train the algorithm offline
+algo.fit(dataset, n_steps=10000, n_steps_per_epoch=1000)
+# algo = TD3PlusBC(actor_learning_rate=1e-4, alpha=2.5)
+# algo.fit(dataset, n_epochs=200)
+# algo.save_model("td3bc_model.d3")