first-upload

.gitignore

@@ -0,0 +1,4 @@
+Results/
+.DS_Store
+.idea/
+Scripts/__pycache__/

Main.py

@@ -0,0 +1,80 @@
+from Scripts.Algorithm import train, evaluateMARLNonLocal, evaluateMARLLocal
+from Scripts.Parameters import ParseInput
+import time
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+if __name__ == '__main__':
+    args = ParseInput()
+
+    t0 = time.time()
+
+    indexN = 0
+    valueLocalArray = np.zeros(args.numN)
+    valueLocalArraySD = np.zeros(args.numN)
+
+    valueNonLocalArray = np.zeros(args.numN)
+    valueNonLocalArraySD = np.zeros(args.numN)
+
+    ErrorArray = np.zeros(args.numN)
+    ErrorArraySD = np.zeros(args.numN)
+
+    NVec = np.zeros(args.numN)
+
+    if args.train:
+        print('Training is in progress.')
+        train(args)
+
+    print('Evaluation is in progress.')
+    while indexN < args.numN:
+        N = args.minN + indexN * args.divN
+        NVec[indexN] = N
+
+        for _ in range(0, args.maxSeed):
+            valueLocal = evaluateMARLLocal(args, N)
+            valueLocal = np.array(valueLocal.detach())
+
+            valueLocalArray[indexN] += valueLocal/args.maxSeed
+            valueLocalArraySD[indexN] += valueLocal ** 2 / args.maxSeed
+
+            valueNonLocal = evaluateMARLNonLocal(args, N)
+            valueNonLocal = np.array(valueNonLocal.detach())
+
+            valueNonLocalArray[indexN] += valueNonLocal/args.maxSeed
+            valueNonLocalArraySD[indexN] += valueNonLocal**2/args.maxSeed
+
+            Error = np.abs(valueNonLocal - valueLocal)
+            ErrorArray[indexN] += Error/args.maxSeed
+            ErrorArraySD[indexN] += Error**2/args.maxSeed
+
+        indexN += 1
+        print(f'N: {N}')
+
+    valueLocalArraySD = np.sqrt(np.maximum(0, valueLocalArraySD - valueLocalArray ** 2))
+    valueNonLocalArraySD = np.sqrt(np.maximum(0, valueNonLocalArraySD - valueNonLocalArray ** 2))
+    ErrorArraySD = np.sqrt(np.maximum(0, ErrorArraySD - ErrorArray ** 2))
+
+    if not os.path.exists('Results'):
+        os.mkdir('Results')
+
+    plt.figure()
+    plt.xlabel('N')
+    plt.ylabel('Values')
+    plt.plot(NVec, valueLocalArray, label='Local')
+    plt.fill_between(NVec, valueLocalArray - valueLocalArraySD, valueLocalArray + valueLocalArraySD, alpha=0.3)
+    plt.plot(NVec, valueNonLocalArray, label='Non-Local')
+    plt.fill_between(NVec, valueNonLocalArray - valueNonLocalArraySD, valueNonLocalArray + valueNonLocalArraySD, alpha=0.3)
+    plt.legend()
+    plt.savefig(f'Results/Values.png')
+
+    plt.figure()
+    plt.xlabel('N')
+    plt.ylabel('Error')
+    plt.plot(NVec, ErrorArray)
+    plt.fill_between(NVec, ErrorArray - ErrorArraySD, ErrorArray + ErrorArraySD, alpha=0.3)
+    plt.savefig(f'Results/Error.png')
+
+    t1 = time.time()
+
+    print(f'Elapsed time is {t1-t0} sec')

README.md

@@ -0,0 +1,35 @@
+# Introduction
+
+This repository contains codes that are used for generating numerical results in the following paper: 
+
+"On the Near-Optimality of Local Policies in Large Cooperative 
+Multi-Agent Reinforcement Learning", Transactions on Machine Learning Research, 2022.
+
+# Parameters
+
+Various parameters used in the experiments can be found in Scripts/Parameters.py file.
+
+# Results
+
+Generated results will be stored in Results folder (will be created on the fly).
+Some pre-generated results are available for display in the Display folder. Specifically,
+Fig. 1 depicts the percentage error between the values generated by local and non-local policies in an N-agent system
+as a function of N.
+
+# Run Experiments
+
+```
+python3 Main.py
+```
+
+# Command Line Options
+
+Various command line options are given below:
+
+```
+--train : if training is required from scratch, otherwise a pre-trained model will be used   
+--minN : minimum value of N  
+--numN : number of N values  
+--divN : difference between two consecutive N values  
+--maxSeed: number of random seeds 
+```