Add maze 10 res

DeepQSingle.py

@@ -663,7 +663,7 @@ def trainDQN(args):
     state_index = -1
 
     for _ in range(int(args.iterations)):
-        state = 5
+        state = 18
         
         if state in obstacles_loc or state == TARGET_LOC:
             continue

DeepQV2.py

@@ -64,7 +64,7 @@ START = 0.5
 #Run params
 START_STATE = 1
 MAX_EPISODES = 60
-EMBTOGGLE = 0
+EMBTOGGLE = 1
 DIMENSION = 20
 TARGET_LOC = 399
 EMBEDPATH = "./Embeddings/"
@@ -107,9 +107,9 @@ BATCH_SIZE = 50
 #Save params
 state_index = -1
 rew_arr = []
-rewardAxis = np.zeros((20,60))
-stepsAxis = np.zeros((20,60))
-partRew = [[] for i in range(20)]
+rewardAxis = np.zeros((10,60))
+stepsAxis = np.zeros((10,60))
+partRew = [[] for i in range(10)]
 globalTotSteps = 0
 
 

loadarray.py

@@ -27,9 +27,9 @@ def transformData2(arr):
             a.append([i,j,arr[i][j]])
     return a
 
-rews1 = np.load('./Results/FPTS/maze6_GS20.npy')
-rews2 = np.load('./Results/FPTS/maze6_GS.npy')
-rews3 = np.load('./Results/FPTS/maze6_GS40.npy')
+rews1 = np.load('./Results/FPTS/maze10_NERD.npy')
+rews2 = np.load('./Results/FPTS/maze10_HOPE.npy')
+rews3 = np.load('./Results/RW2/maze10_matrix.npy')
 rews4 = np.load('./Results/FPTS/maze6_APP.npy')
 
 
@@ -105,11 +105,11 @@ ax = sns.lineplot(x='steps', y='reward',color='purple', data=df50, ci=80)
 #     plt.plot(rews2[i])
 # plt.legend(['0','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19'])
 
-plt.legend(['APP-1000','APP-2000','AP-4000','APP'], loc='upper right', fontsize='small')
+plt.legend(['APP','HOPE','NERD','Matrix','DeepWalk'], loc='upper right', fontsize='small')
 
 plt.title('Maze 1 APP')
 plt.ylim(-50,20)
-#plt.xlim(0,110)
+plt.xlim(0,120)
 plt.xlabel('Time Steps x50')
 plt.ylabel('Average Cumulative Reward')