Implemented Maxwell distribution for rewards

entities/player.py

@@ -62,6 +62,7 @@ class Player(pygame.sprite.Sprite):
                     chkpt_dir,
                     no_load=False):
 
+        self.max_num_enemies = len(self.distance_direction_from_enemy)
         self.get_current_state()
         self.num_features = len(self.state_features)
 
@@ -158,9 +159,17 @@ class Player(pygame.sprite.Sprite):
 
         self.reward_features = [
             self.stats.exp,
+
             2*np.exp(-nearest_dist**2),
-            np.exp(-nearest_enemy.stats.health),
-            -np.exp(-self.stats.health**2)
+
+            1/(np.exp((nearest_enemy.stats.health -
+               nearest_enemy.stats.monster_info['health'])/nearest_enemy.stats.monster_info['health'])) - 1,
+
+            1/(np.exp((len(self.distance_direction_from_enemy) -
+               self.max_num_enemies)/self.max_num_enemies)) - 1,
+
+            1 - 1/(np.exp((self.stats.health -
+                   self.stats.stats['health'])/self.stats.stats['health']))
             if not self.is_dead() > 0 else -1
         ]
 
@@ -233,7 +242,7 @@ class Player(pygame.sprite.Sprite):
             self.stats.energy_recovery()
 
         else:
-            self.stats.exp = max(0, self.stats.exp - .01)
+            self.stats.exp = max(-1, self.stats.exp - .1)
 
         # Refresh player based on input and animate
         self.get_status()

pneuma.py

@@ -1,4 +1,4 @@
-import os
+import random
 import argparse
 import torch as T
 import numpy as np
@@ -55,7 +55,7 @@ if __name__ == "__main__":
 
     parser.add_argument('--horizon',
                         type=int,
-                        default=200,
+                        default=2048,
                         help="The number of steps per update")
 
     parser.add_argument('--show_pg',
@@ -100,6 +100,7 @@ if __name__ == "__main__":
 
     args = parser.parse_args()
 
+    random.seed(args.seed)
     np.random.seed(args.seed)
     T.manual_seed(args.seed)
 
@@ -219,12 +220,6 @@ if __name__ == "__main__":
 
                 print(f"Models saved to {chkpt_path}")
 
-            else:
-                print(f"\nScore this round for player {player.player_id}:\
-                      {player.stats.exp}")
-
-
-
     # End of training session
     print("End of episodes.\
         \nExiting game...")
@@ -264,4 +259,5 @@ if __name__ == "__main__":
     for total in total_loss:
         plt.plot(total)
     plt.savefig(f"{figure_folder}/total_loss.png")
+
     game.quit()