Preparation v1.3.0

.github/workflows/ci.yml

@@ -47,7 +47,7 @@ jobs:
     if: needs.deps_gen.outputs.trigger_build == 'true'
     strategy:
       matrix:
-        python-version: ['3.9', '3.10', '3.11', '3.12']
+        python-version: ['3.9', '3.10', '3.11']
 
     steps:
     - name: Checkout Codes
@@ -83,7 +83,7 @@ jobs:
     if: needs.deps_gen.outputs.trigger_build == 'true'
     strategy:
       matrix:
-        python-version: ['3.12']
+        python-version: ['3.11']
 
     steps:
     - name: Checkout Codes
@@ -115,7 +115,7 @@ jobs:
     if: needs.deps_gen.outputs.trigger_build == 'true'
     strategy:
       matrix:
-        python-version: ['3.12']
+        python-version: ['3.11']
 
     steps:
     - name: Checkout Codes
@@ -143,7 +143,7 @@ jobs:
     if: needs.deps_gen.outputs.trigger_build == 'true'
     strategy:
       matrix:
-        python-version: ['3.12']
+        python-version: ['3.11']
 
     steps:
     - name: Checkout Codes

.readthedocs.yaml

@@ -10,7 +10,7 @@ version: 2
 build:
  os: ubuntu-22.04
  tools:
-   python: "3.12"
+   python: "3.11"
 
 
 # Build documentation in the docs/ directory with Sphinx

doc/rtd/content/03_machine_learning/mlpro_gt/sub/03_native.rst

@@ -1,5 +1,5 @@
 .. _target_doc_gt_native:
-MLPro-GT-Native
+MLPro-GT-Native - Native Games
 ---------------------------------
 
 .. toctree::

doc/rtd/content/03_machine_learning/mlpro_gt/sub/04_dg.rst

@@ -1,5 +1,5 @@
 .. _target_doc_gt:
-MLPro-GT-DG
+MLPro-GT-DG - Dynamic Games
 ---------------------------------
 
 .. toctree::

doc/rtd/content/03_machine_learning/mlpro_gt/sub/gettingstarted/01_native.rst

@@ -1,5 +1,5 @@
-MLPro-GT-Native
--------------------
+MLPro-GT-Native - Native Games
+------------------------------
 
 Here is a concise series to introduce all users to the MLPro-GT-Native in a practical way, whether you are a first-timer or an experienced MLPro user.
 

doc/rtd/content/03_machine_learning/mlpro_gt/sub/gettingstarted/02_dg.rst

@@ -1,5 +1,5 @@
-MLPro-GT-DG
--------------------
+MLPro-GT-DG - Dynamic Games
+---------------------------
 
 Here is a concise series to introduce all users to the MLPro-GT-DG in a practical way, whether you are a first-timer or an experienced MLPro user.
 

doc/rtd/content/03_machine_learning/mlpro_gt/sub/native/games/pool/prisonersdilemma_2p.rst

@@ -55,8 +55,8 @@ The outcomes and associated payoffs for each player depend on the combination of
 
 **Cross References**
 
-    + :ref:`API Reference <target_api_gt_pool_2pprisoners>`
     + :ref:`Howto GT-Native-001: 2P Prisoners Dilemma <Howto GTN 001>`
+    + :ref:`API Reference <target_api_gt_pool_2pprisoners>`
 
 **Citation**
 

doc/rtd/content/03_machine_learning/mlpro_gt/sub/native/games/pool/prisonersdilemma_3p.rst

@@ -55,8 +55,8 @@ The outcomes and associated payoffs for each player depend on the combination of
 
 **Cross References**
 
-    + :ref:`API Reference <target_api_gt_pool_3pprisoners>`
     + :ref:`Howto GT-Native-002: 3P Prisoners Dilemma <Howto GTN 002>`
+    + :ref:`API Reference <target_api_gt_pool_3pprisoners>`
 
 **Citation**
 

doc/rtd/content/03_machine_learning/mlpro_gt/sub/native/games/pool/rockpaperscissors.rst

@@ -63,8 +63,8 @@ Here, the first value in each pair represents the payoff to the row coalition, a
 
 **Cross References**
 
-    + :ref:`API Reference <target_api_gt_pool_rps>`
     + :ref:`Howto GT-Native-003: Rock, Paper, Scissors <Howto GTN 003>`
+    + :ref:`API Reference <target_api_gt_pool_rps>`
 
 **Citation**
 

doc/rtd/content/03_machine_learning/mlpro_gt/sub/native/games/pool/routingproblems_3p.rst

@@ -86,8 +86,8 @@ The figure below shows the traveling time from one node to another, where each p
 
 **Cross References**
 
-    + :ref:`API Reference <target_api_gt_pool_3pconges>`
     + :ref:`Howto GT-Native-005: 3P Routing Problems <Howto GTN 005>`
+    + :ref:`API Reference <target_api_gt_pool_3pconges>`
 
 **Citation**
 

doc/rtd/content/03_machine_learning/mlpro_gt/sub/native/games/pool/supplydemand_3p.rst

@@ -67,8 +67,8 @@ The table below shows the sales price per product based on the quantity produced
 
 **Cross References**
 
-    + :ref:`API Reference <target_api_gt_pool_3psd>`
     + :ref:`Howto GT-Native-004: 3P Supply and Demand <Howto GTN 004>`
+    + :ref:`API Reference <target_api_gt_pool_3psd>`
 
 **Citation**
 

doc/rtd/content/03_machine_learning/mlpro_gt/sub/native/solvers/pool/minmaxgreedy.rst

@@ -21,9 +21,9 @@ The minimum greedy can be imported via:
     
 
 **Cross Reference**
-    + :ref:`API Reference <target_native_pool_gt_solvers_random>`
     + :ref:`Howto GT-Native-002: 3P Prisoners Dilemma <Howto GTN 002>`
     + :ref:`Howto GT-Native-005: 3P Routing Problems <Howto GTN 005>`
+    + :ref:`API Reference <target_native_pool_gt_solvers_random>`
 
 
 **Citation**

doc/rtd/content/03_machine_learning/mlpro_gt/sub/native/solvers/pool/randomsolver.rst

@@ -17,8 +17,9 @@ The random solvers can be imported via:
     
 
 **Cross Reference**
-    + :ref:`API Reference <target_native_pool_gt_solvers_random>`
+
     + :ref:`Howto GT-Native-001: 2P Prisoners Dilemma <Howto GTN 001>`
+    + :ref:`API Reference <target_native_pool_gt_solvers_random>`
 
 
 **Citation**