Update AINetworkDefense_Competitive/config.py

AINetworkDefense_Competitive/config.py

@@ -2,30 +2,43 @@ import os
 import tensorflow as tf
 
 class Config:
+
     # Core Environment Framework
-    MODE = 'competitive'  # Options: 'competitive' or 'cooperative'
+    MODE = 'cooperative'  # Options: 'competitive' or 'cooperative'
     USE_GPU = True
     DEVICE = '/GPU:0' if USE_GPU else '/CPU:0'
-    NUM_ENVS = 10 if MODE == 'competitive' else 20 #10 for Air and Fire, 20 for Air and Ice
-    MAX_STEPS_PER_EPISODE = 2048 
+    MAX_STEPS_PER_EPISODE = 2048  # Unified trajectory sizing
     
-    # File Management Shards 
-    LOG_DIR = "./DefenseAI_Competitive/logs"
-    CHECKPOINT_DIR = "./DefenseAI_Competitive/checkpoints"
+    # DYNAMIC ENVIRONMENT LOGS, CHECKPOINTS & WORKER ALLOCATION
+    if str(MODE).lower() == 'competitive':
+        NUM_ENVS = 10
+        LOG_DIR = "./DefenseAI_Competitive/logs"
+        CHECKPOINT_DIR = "./DefenseAI_Competitive/checkpoints"
+    elif str(MODE).lower() == 'cooperative':
+        NUM_ENVS = 20
+        LOG_DIR = "./DefenseAI_Cooperative/logs"
+        CHECKPOINT_DIR = "./DefenseAI_Cooperative/checkpoints"
+    else:
+        print(f"⚠️ Warning: Unrecognized Config.MODE '{MODE}'. Defaulting to 'competitive' mode.") #Or cooperative mode based on your preference
+        MODE = 'competitive'
+        NUM_ENVS = 12
+        LOG_DIR = "./DefenseAI_Fallback/logs"
+        CHECKPOINT_DIR = "./DefenseAI_Fallback/checkpoints"
+
     CHECKPOINT_INTERVAL = 10
-    
-    # Neural Network Topologies 
+
+    # Neural Network Topologies
     ACTOR_LAYERS = (128, 64)
     CRITIC_LAYERS = (128, 64)
-    
+
     # Reinforcement Learning Core Hyperparameters
     GAMMA = 0.99
     BUFFER_MAX_LENGTH = 100000
     BATCH_SIZE = 64
-    TOTAL_EPISODES = 5000
+    TOTAL_EPISODES = 6000
 
     # Programmable Cyber Range Network Topology 
-    # 9 Total Node Count but can scale dynamically !
+    # 9 Total Node Count but can scale dynamically
     NETWORK_TOPOLOGY = {
         "subnets": {
             "public_dmz":      {"num_hosts": 2, "base_vulnerability": 0.7},  # Entry point web targets
@@ -44,7 +57,7 @@ class Config:
     # Symmetrical Learning Rate Schedulers
     BASE_LR_RED = 1e-4 
     BASE_LR_BLUE = 1e-4 
-    
+
     @classmethod
     def get_red_lr_schedule(cls):
         """Returns an exponential decay scheduler for the Red Agent."""