GT-Native: draft howto 2p Prisoners' Dilemma #758

fhswf · Sep 22, 2023 · 21984aa · 21984aa
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diff --git a/...ent/99_appendices/appendix2/sub/core/mlpro_gt/images/MLPro-GT-Native_class_diagram.drawio b/...ent/99_appendices/appendix2/sub/core/mlpro_gt/images/MLPro-GT-Native_class_diagram.drawio
@@ -1,4 +1,4 @@
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diff --git a/...ces/appendix2/sub/core/mlpro_gt/images/MLPro-GT-Native_class_diagram.drawio.png b/...ces/appendix2/sub/core/mlpro_gt/images/MLPro-GT-Native_class_diagram.drawio.png
diff --git a/...t_native_001_run_prisonners_dilemma_2p.py → ...to_gt_native_001_prisonners_dilemma_2p.py b/...t_native_001_run_prisonners_dilemma_2p.py → ...to_gt_native_001_prisonners_dilemma_2p.py
@@ -1,7 +1,7 @@
 ## -------------------------------------------------------------------------------------------------
 ## -- Project : MLPro - A Synoptic Framework for Standardized Machine Learning Tasks
 ## -- Package : mlpro.gt.examples
-## -- Module  : howto_gt_native_001_run_prisonners_dilemma_2p.py
+## -- Module  : howto_gt_native_001_prisonners_dilemma_2p.py
 ## -------------------------------------------------------------------------------------------------
 ## -- History :
 ## -- yyyy-mm-dd  Ver.      Auth.    Description
@@ -22,4 +22,34 @@
 
 3) How to analyse the game
     
-"""
+"""
+
+from mlpro.gt.native.basics import *
+from mlpro.gt.pool.native.games.prisonersdilemma_2p import *
+from pathlib import Path
+
+
+
+
+if __name__ == "__main__":
+    cycle_limit = 1
+    logging     = Log.C_LOG_WE
+    visualize   = False
+    path        = str(Path.home())
+
+else:
+    cycle_limit = 1
+    logging     = Log.C_LOG_NOTHING
+    visualize   = False
+    path        = None
+
+PD2P_Game = PrisonersDilemma2PGame()
+
+training = GTTraining(
+        p_game_cls=PrisonersDilemma2PGame,
+        p_cycle_limit=cycle_limit,
+        p_path=path,
+        p_visualize=visualize,
+        p_logging=logging )
+
+training.run()
diff --git a/src/mlpro/gt/native/basics.py b/src/mlpro/gt/native/basics.py
@@ -14,10 +14,11 @@
 ## --                                - Enhancement of GTStrategy
 ## --                                - Add TransferFunction as another option on GTFunction
 ## -- 2023-09-21  1.0.3     SY       Refactoring
+## -- 2023-09-22  1.0.4     SY       Update __init__ and _setup of GTGame
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.0.3 (2023-09-21)
+Ver. 1.0.4 (2023-09-22)
 
 This module provides model classes for tasks related to a Native Game Theory.
 """
@@ -414,11 +415,19 @@ def __init__(self,
             self._random_solver = False
             self._list_solvers  = [p_solver]
         self._num_solvers = len(self._list_solvers)
-        self.switch_solver()
 
         self._visualize = p_visualize
         self._logging   = p_logging
         self._param     = p_param
+
+        GTSolver.__init__(self,
+                          p_strategy_space = self._list_solvers[0].get_strategy_space(),
+                          p_id = self._list_solvers[0].get_id(),
+                          p_visualize = self._visualize,
+                          p_logging = self._logging,
+                          **self._param)
+
+        self.switch_solver()
 
 
 ## -------------------------------------------------------------------------------------------------
@@ -476,7 +485,8 @@ def get_solver(self) -> GTSolver:
     def switch_solver(self):
 
         if len(self._list_solvers) == 1:
-            self.log(self.C_LOG_TYPE_I, 'Player %s is keeping the same solver %s'%(self._id, self._solver.get_id()))
+            self._solver = self._list_solvers[0]
+            self.log(self.C_LOG_TYPE_I, '%s is keeping the same solver %s'%(self.get_name(), self._solver.get_id()))
         else:
             if self._random_solver:
                 rnd             = random.randint(0, self._num_solvers-1)
@@ -861,17 +871,17 @@ class GTGame (Scenario):
 
 ## -------------------------------------------------------------------------------------------------
     def __init__(self,
-                 p_payoff_matrix:GTPayoffMatrix,
-                 p_visualize:bool = False,
-                 p_logging = Log.C_LOG_ALL):
+                 p_mode=Mode.C_MODE_SIM,
+                 p_ada=False,
+                 p_cycle_limit=1,
+                 p_visualize:bool=False,
+                 p_logging=Log.C_LOG_ALL):
 
-        super().__init__(p_mode=Mode.C_MODE_SIM,
-                         p_ada=False,
-                         p_cycle_limit=1,
+        super().__init__(p_mode=p_mode,
+                         p_ada=p_ada,
+                         p_cycle_limit=p_cycle_limit,
                          p_visualize=p_visualize,
                          p_logging=p_logging)
-
-        self._payoff        = p_payoff_matrix
         self._strategies    = None
 
         self.connect_data_logger()
@@ -880,7 +890,7 @@ def __init__(self,
 ## -------------------------------------------------------------------------------------------------
     def _setup(self, p_mode, p_ada:bool, p_visualize:bool, p_logging) -> Model:
         """
-        Custom setup of GT Game.
+        Custom setup of GT Game. Payoff matrix has to be defined here as self._payoff.
 
         Parameters
         ----------

diff --git a/src/mlpro/gt/pool/native/games/prisonersdilemma_2p.py b/src/mlpro/gt/pool/native/games/prisonersdilemma_2p.py
@@ -67,7 +67,7 @@ class PrisonersDilemma2PGame (GTGame):
     def _setup(self, p_mode, p_ada:bool, p_visualize:bool, p_logging) -> Model:
 
         solver1 = RandomSolver(
-            p_strategy_space=MSpace().add_dim('RStr','Z','Random Strategy','','','',[0,1]),
+            p_strategy_space=MSpace().add_dim(Dimension('RStr','Z','Random Strategy','','','',[0,1])),
             p_id=1,
             p_visualize=p_visualize,
             p_logging=p_logging
@@ -117,6 +117,11 @@ def _setup(self, p_mode, p_ada:bool, p_visualize:bool, p_logging) -> Model:
             )
         competition.add_coalition(coal1)
         competition.add_coalition(coal2)
+
+        self._payoff = GTPayoffMatrix(
+            p_function=PayoffFunction_PD2P,
+            p_player_ids=[1,2]
+        )
 
         return competition