diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..26d3352
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml
diff --git a/.idea/PPO-PyTorch.iml b/.idea/PPO-PyTorch.iml
new file mode 100644
index 0000000..f8ab605
--- /dev/null
+++ b/.idea/PPO-PyTorch.iml
@@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="JAVA_MODULE" version="4">
+  <component name="FacetManager">
+    <facet type="Python" name="Python">
+      <configuration sdkName="Python 3.8 (Python3.8)" />
+    </facet>
+  </component>
+  <component name="NewModuleRootManager" inherit-compiler-output="true">
+    <exclude-output />
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+    <orderEntry type="library" name="Python 3.8 (Python3.8) interpreter library" level="application" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..d9aedac
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_14" default="false" project-jdk-name="Python 3.8 (PPO-PyTorch)" project-jdk-type="Python SDK">
+    <output url="file://$PROJECT_DIR$/out" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..497dbb8
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/PPO-PyTorch.iml" filepath="$PROJECT_DIR$/.idea/PPO-PyTorch.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
index 7777565..4321bbe 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,5 @@
-MIT License
 
-Copyright (c) 2018 Nikhil Barhate
+Copyright (c) 2022 Andrew Lobbezoo
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
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diff --git a/PPO_logs/BipedalWalker-v2/PPO_BipedalWalker-v2_log_0.csv b/PPO_logs/BipedalWalker-v2/PPO_BipedalWalker-v2_log_0.csv
deleted file mode 100644
index 141e0a3..0000000
--- a/PPO_logs/BipedalWalker-v2/PPO_BipedalWalker-v2_log_0.csv
+++ /dev/null
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diff --git a/PPO_logs/BipedalWalker-v2/PPO_BipedalWalker-v2_log_1.csv b/PPO_logs/BipedalWalker-v2/PPO_BipedalWalker-v2_log_1.csv
deleted file mode 100644
index 8b8ea85..0000000
--- a/PPO_logs/BipedalWalker-v2/PPO_BipedalWalker-v2_log_1.csv
+++ /dev/null
@@ -1,69 +0,0 @@
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diff --git a/PPO_logs/CartPole-v1/PPO_CartPole-v1_log_0.csv b/PPO_logs/CartPole-v1/PPO_CartPole-v1_log_0.csv
deleted file mode 100644
index db1bd87..0000000
--- a/PPO_logs/CartPole-v1/PPO_CartPole-v1_log_0.csv
+++ /dev/null
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diff --git a/PPO_logs/CartPole-v1/PPO_CartPole-v1_log_1.csv b/PPO_logs/CartPole-v1/PPO_CartPole-v1_log_1.csv
deleted file mode 100644
index ece6c59..0000000
--- a/PPO_logs/CartPole-v1/PPO_CartPole-v1_log_1.csv
+++ /dev/null
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diff --git a/PPO_logs/CartPole-v1/PPO_CartPole-v1_log_2.csv b/PPO_logs/CartPole-v1/PPO_CartPole-v1_log_2.csv
deleted file mode 100644
index 5089d1a..0000000
--- a/PPO_logs/CartPole-v1/PPO_CartPole-v1_log_2.csv
+++ /dev/null
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diff --git a/PPO_logs/LunarLander-v2/PPO_LunarLander-v2_log_0.csv b/PPO_logs/LunarLander-v2/PPO_LunarLander-v2_log_0.csv
deleted file mode 100644
index f80a737..0000000
--- a/PPO_logs/LunarLander-v2/PPO_LunarLander-v2_log_0.csv
+++ /dev/null
@@ -1,1667 +0,0 @@
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diff --git a/PPO_logs/LunarLander-v2/PPO_LunarLander-v2_log_1.csv b/PPO_logs/LunarLander-v2/PPO_LunarLander-v2_log_1.csv
deleted file mode 100644
index f9094aa..0000000
--- a/PPO_logs/LunarLander-v2/PPO_LunarLander-v2_log_1.csv
+++ /dev/null
@@ -1,1667 +0,0 @@
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diff --git a/PPO_logs/LunarLander-v2/PPO_LunarLander-v2_log_2.csv b/PPO_logs/LunarLander-v2/PPO_LunarLander-v2_log_2.csv
deleted file mode 100644
index b15b66a..0000000
--- a/PPO_logs/LunarLander-v2/PPO_LunarLander-v2_log_2.csv
+++ /dev/null
@@ -1,1667 +0,0 @@
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diff --git a/PPO_logs/PandaReachDense-v2/PPO_PandaReachDense-v2_log_0.csv b/PPO_logs/PandaReachDense-v2/PPO_PandaReachDense-v2_log_0.csv
new file mode 100644
index 0000000..23ca62c
--- /dev/null
+++ b/PPO_logs/PandaReachDense-v2/PPO_PandaReachDense-v2_log_0.csv
@@ -0,0 +1,126 @@
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diff --git a/PPO_logs/PandaReachDense-v2/PPO_PandaReachDense-v2_log_1.csv b/PPO_logs/PandaReachDense-v2/PPO_PandaReachDense-v2_log_1.csv
new file mode 100644
index 0000000..79a89c9
--- /dev/null
+++ b/PPO_logs/PandaReachDense-v2/PPO_PandaReachDense-v2_log_1.csv
@@ -0,0 +1,126 @@
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diff --git a/PPO_logs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_log_0.csv b/PPO_logs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_log_0.csv
deleted file mode 100644
index 95e2917..0000000
--- a/PPO_logs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_log_0.csv
+++ /dev/null
@@ -1,1501 +0,0 @@
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diff --git a/PPO_logs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_log_1.csv b/PPO_logs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_log_1.csv
deleted file mode 100644
index fd0993e..0000000
--- a/PPO_logs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_log_1.csv
+++ /dev/null
@@ -1,1501 +0,0 @@
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diff --git a/PPO_logs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_log_2.csv b/PPO_logs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_log_2.csv
deleted file mode 100644
index 04f729c..0000000
--- a/PPO_logs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_log_2.csv
+++ /dev/null
@@ -1,1501 +0,0 @@
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diff --git a/PPO_logs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_log_0.csv b/PPO_logs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_log_0.csv
deleted file mode 100644
index 7e76c36..0000000
--- a/PPO_logs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_log_0.csv
+++ /dev/null
@@ -1,1501 +0,0 @@
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-9350,1366000,1411.2631
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-9360,1370000,1385.7219
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-9369,1376000,908.5963
-9372,1378000,1463.6835
-9375,1380000,809.952
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-9380,1384000,1795.2468
-9382,1386000,1984.6055
-9384,1388000,2014.08
-9388,1390000,995.6398
-9393,1392000,828.5536
-9395,1394000,1997.0851
-9397,1396000,1746.8634
-9400,1398000,1237.2924
-9403,1400000,1624.8315
-9407,1402000,738.099
-9411,1404000,1059.7558
-9414,1406000,1555.4545
-9416,1408000,1111.5321
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-9451,1432000,661.1392
-9456,1434000,896.879
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-9460,1438000,2017.6068
-9462,1440000,1903.494
-9465,1442000,1279.0929
-9468,1444000,1512.5339
-9471,1446000,1735.1727
-9473,1448000,1959.8661
-9476,1450000,1010.844
-9480,1452000,1124.2089
-9484,1454000,842.3481
-9489,1456000,928.8753
-9492,1458000,1038.1439
-9495,1460000,1430.0919
-9499,1462000,1192.8758
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-9507,1468000,1242.6657
-9510,1470000,1341.451
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-9517,1474000,1117.8216
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-9527,1480000,1141.8675
-9530,1482000,1906.4233
-9534,1484000,863.0112
-9537,1486000,1575.3605
-9540,1488000,1343.228
-9542,1490000,2013.4785
-9547,1492000,688.3698
-9549,1494000,1954.1357
-9553,1496000,1016.4465
-9555,1498000,1387.6997
-9561,1500000,817.5201
-9563,1502000,1990.7815
-9565,1504000,2057.4348
-9567,1506000,1649.6544
-9570,1508000,1724.4072
-9573,1510000,1471.6248
-9575,1512000,2092.4396
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-9589,1520000,1247.0925
-9591,1522000,2031.2988
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-9602,1530000,1157.0816
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-9611,1536000,1308.2908
-9613,1538000,2029.6294
-9615,1540000,2050.2281
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-9648,1566000,2003.4987
-9650,1568000,2051.7765
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-9664,1578000,1016.0209
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-9675,1584000,891.1172
-9677,1586000,1618.8935
-9680,1588000,1930.9553
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-9762,1648000,1761.3808
-9764,1650000,1998.9413
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-9780,1660000,1661.4918
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-9864,1722000,2006.9685
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-9877,1730000,1377.6225
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-9948,1790000,1740.2256
-9951,1792000,1491.8433
-9953,1794000,2009.5029
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-9981,1814000,2148.3306
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-9999,1828000,1334.4
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-10011,1836000,1591.345
-10013,1838000,1988.5881
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-10028,1848000,1709.3234
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-10540,2258000,2151.3426
-10542,2260000,2377.3059
-10544,2262000,2004.7654
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-10552,2270000,2374.7793
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-10610,2318000,2173.5235
-10613,2320000,1954.6904
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-10631,2334000,2004.3198
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-10732,2420000,1975.7974
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diff --git a/PPO_logs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_log_1.csv b/PPO_logs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_log_1.csv
deleted file mode 100644
index 1ef2685..0000000
--- a/PPO_logs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_log_1.csv
+++ /dev/null
@@ -1,1501 +0,0 @@
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-8554,892000,759.155
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-8578,902000,535.5725
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-8592,908000,843.3354
-8596,910000,766.3518
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-8608,916000,1103.1038
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-8617,920000,752.7456
-8623,922000,557.4579
-8627,924000,973.7451
-8630,926000,802.898
-8634,928000,1109.3071
-8638,930000,618.8207
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-8646,934000,840.7813
-8650,936000,1002.5243
-8652,938000,1201.6268
-8657,940000,875.641
-8661,942000,814.4333
-8664,944000,999.3228
-8667,946000,949.5314
-8672,948000,709.4217
-8675,950000,919.3251
-8680,952000,678.8741
-8682,954000,1582.543
-8685,956000,1163.2136
-8687,958000,1184.8204
-8690,960000,1116.8783
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-8697,964000,1080.388
-8702,966000,647.097
-8705,968000,674.3164
-8709,970000,1138.6645
-8712,972000,1104.7363
-8715,974000,941.5384
-8718,976000,1338.1081
-8720,978000,1407.377
-8723,980000,1116.8115
-8726,982000,955.5676
-8728,984000,1571.2016
-8730,986000,1610.6875
-8735,988000,891.0973
-8737,990000,1197.9884
-8740,992000,593.2144
-8744,994000,794.7923
-8747,996000,1433.3731
-8749,998000,1066.7159
-8752,1000000,791.5977
-8754,1002000,1562.4618
-8756,1004000,1144.2511
-8759,1006000,1260.6887
-8762,1008000,1214.9375
-8766,1010000,1159.2507
-8769,1012000,1172.8436
-8772,1014000,1054.7397
-8776,1016000,864.214
-8780,1018000,684.7901
-8782,1020000,1490.6573
-8789,1022000,570.7179
-8793,1024000,895.4656
-8797,1026000,892.3055
-8801,1028000,748.0585
-8804,1030000,1353.321
-8807,1032000,1202.7686
-8809,1034000,1768.2163
-8813,1036000,445.3062
-8817,1038000,776.7541
-8819,1040000,1319.7064
-8822,1042000,1628.9815
-8824,1044000,1157.4729
-8827,1046000,892.0321
-8830,1048000,1446.9866
-8835,1050000,593.6104
-8838,1052000,1356.3775
-8841,1054000,1153.3442
-8843,1056000,1117.599
-8847,1058000,1246.5112
-8850,1060000,1225.8334
-8852,1062000,1418.5478
-8856,1064000,840.3406
-8859,1066000,1246.057
-8861,1068000,1757.165
-8863,1070000,1822.9518
-8867,1072000,595.9528
-8871,1074000,1104.0772
-8874,1076000,1350.9802
-8877,1078000,1088.8006
-8880,1080000,1201.8271
-8883,1082000,1168.2072
-8885,1084000,1582.2584
-8887,1086000,1762.6799
-8890,1088000,972.4371
-8893,1090000,1295.4418
-8896,1092000,1166.0021
-8898,1094000,1789.0813
-8901,1096000,1061.9066
-8906,1098000,887.0188
-8910,1100000,840.8202
-8912,1102000,1275.2614
-8916,1104000,1322.2144
-8919,1106000,752.155
-8922,1108000,1420.1886
-8924,1110000,813.505
-8926,1112000,1842.5437
-8929,1114000,958.0807
-8931,1116000,1283.599
-8935,1118000,1205.9975
-8937,1120000,1764.4628
-8939,1122000,1569.6064
-8942,1124000,1201.7578
-8946,1126000,744.4404
-8950,1128000,1339.8693
-8954,1130000,574.2628
-8959,1132000,964.9375
-8961,1134000,1495.1969
-8964,1136000,1276.0169
-8966,1138000,1124.7747
-8969,1140000,1267.6118
-8974,1142000,1026.9919
-8976,1144000,1489.521
-8980,1146000,1066.393
-8983,1148000,1228.4663
-8987,1150000,885.1207
-8990,1152000,1150.4994
-8992,1154000,1249.2892
-8994,1156000,1642.0214
-8999,1158000,910.9845
-9005,1160000,682.4149
-9010,1162000,661.6883
-9013,1164000,1279.3911
-9018,1166000,664.4471
-9021,1168000,844.9945
-9025,1170000,992.3562
-9028,1172000,1573.0349
-9030,1174000,1113.716
-9034,1176000,1118.2141
-9038,1178000,782.6894
-9043,1180000,944.9885
-9046,1182000,829.6765
-9050,1184000,1153.7642
-9052,1186000,1867.6221
-9055,1188000,934.5441
-9058,1190000,1047.6826
-9064,1192000,744.6094
-9067,1194000,1102.3992
-9070,1196000,1397.3136
-9072,1198000,1358.1959
-9078,1200000,716.65
-9082,1202000,889.6261
-9084,1204000,1153.8595
-9089,1206000,944.4038
-9092,1208000,1126.4779
-9097,1210000,673.3081
-9100,1212000,1254.7279
-9103,1214000,1370.8789
-9105,1216000,1260.954
-9108,1218000,1275.2126
-9110,1220000,1558.7735
-9112,1222000,1692.7304
-9115,1224000,1237.8628
-9119,1226000,1074.4952
-9122,1228000,1175.7945
-9125,1230000,625.9775
-9127,1232000,1628.3172
-9131,1234000,1100.9247
-9135,1236000,858.2082
-9139,1238000,889.1641
-9141,1240000,1183.6704
-9144,1242000,1377.3869
-9147,1244000,1399.3786
-9150,1246000,944.4519
-9153,1248000,1048.2772
-9158,1250000,927.7714
-9160,1252000,1732.1926
-9164,1254000,993.1025
-9169,1256000,743.0585
-9175,1258000,571.3093
-9177,1260000,1522.1616
-9180,1262000,1459.9303
-9183,1264000,1272.0997
-9185,1266000,1455.2108
-9187,1268000,1576.8997
-9189,1270000,1781.8565
-9192,1272000,1525.8113
-9194,1274000,1848.4526
-9196,1276000,1467.0528
-9199,1278000,1263.9948
-9202,1280000,1256.0292
-9205,1282000,1334.58
-9207,1284000,1948.1344
-9209,1286000,1840.1565
-9214,1288000,872.9725
-9216,1290000,1408.138
-9219,1292000,1475.7551
-9221,1294000,1905.9863
-9223,1296000,1527.696
-9228,1298000,678.9438
-9231,1300000,1025.9946
-9235,1302000,1216.0958
-9238,1304000,1406.4245
-9241,1306000,1340.6257
-9244,1308000,1112.7204
-9246,1310000,1143.8062
-9251,1312000,1106.5598
-9255,1314000,750.3021
-9257,1316000,1431.8134
-9260,1318000,1529.5198
-9262,1320000,1409.8713
-9265,1322000,1533.652
-9267,1324000,1257.9268
-9269,1326000,1889.917
-9273,1328000,1090.4574
-9276,1330000,1184.7778
-9281,1332000,905.7987
-9285,1334000,919.7799
-9288,1336000,1219.8009
-9291,1338000,1227.2089
-9295,1340000,817.4216
-9298,1342000,1342.683
-9300,1344000,1250.284
-9304,1346000,1268.0584
-9307,1348000,1152.0249
-9313,1350000,629.441
-9315,1352000,1903.3003
-9318,1354000,1199.2937
-9320,1356000,1210.8759
-9323,1358000,1486.3884
-9326,1360000,1243.8371
-9329,1362000,1252.3862
-9331,1364000,1816.0925
-9334,1366000,983.092
-9336,1368000,1218.2761
-9342,1370000,744.9182
-9346,1372000,812.9725
-9352,1374000,592.5529
-9354,1376000,1870.8175
-9357,1378000,1198.5404
-9360,1380000,1227.0074
-9363,1382000,729.8907
-9367,1384000,914.2845
-9370,1386000,1442.7828
-9372,1388000,1669.9518
-9374,1390000,1840.2383
-9376,1392000,1476.7717
-9379,1394000,1507.2622
-9382,1396000,1359.61
-9384,1398000,1954.8705
-9387,1400000,1425.19
-9390,1402000,1325.8451
-9393,1404000,1203.2826
-9396,1406000,973.716
-9399,1408000,1558.4959
-9401,1410000,1668.2727
-9403,1412000,1893.9058
-9405,1414000,1588.1427
-9408,1416000,1591.3506
-9410,1418000,1889.5467
-9414,1420000,1141.1756
-9417,1422000,1038.4948
-9420,1424000,1323.5702
-9423,1426000,1093.5542
-9426,1428000,1673.7569
-9428,1430000,1177.354
-9433,1432000,955.9474
-9435,1434000,1886.2068
-9438,1436000,1255.66
-9440,1438000,1920.1449
-9442,1440000,1914.1518
-9444,1442000,1808.9097
-9447,1444000,1584.5741
-9449,1446000,1772.057
-9451,1448000,1728.54
-9456,1450000,975.4845
-9458,1452000,1113.4701
-9463,1454000,991.6679
-9467,1456000,1077.363
-9469,1458000,1858.1607
-9472,1460000,1330.4441
-9474,1462000,1511.2843
-9478,1464000,1264.2666
-9480,1466000,1433.387
-9482,1468000,1897.6021
-9485,1470000,1309.0252
-9487,1472000,1957.7516
-9490,1474000,1270.8738
-9492,1476000,1271.3978
-9495,1478000,1755.34
-9498,1480000,1338.632
-9500,1482000,1049.1097
-9502,1484000,1922.1096
-9505,1486000,1316.2052
-9508,1488000,1079.2706
-9513,1490000,1121.6221
-9516,1492000,1313.912
-9518,1494000,1357.8079
-9520,1496000,1961.4868
-9523,1498000,1556.0956
-9526,1500000,877.3092
-9528,1502000,1628.7819
-9531,1504000,1873.9222
-9533,1506000,1922.3057
-9536,1508000,1193.7284
-9539,1510000,1143.7505
-9542,1512000,1581.4389
-9544,1514000,1958.1149
-9546,1516000,1819.9416
-9549,1518000,1469.3355
-9551,1520000,1993.1687
-9553,1522000,1941.198
-9557,1524000,1007.3514
-9559,1526000,1190.1209
-9561,1528000,1946.9805
-9563,1530000,2003.2489
-9567,1532000,1303.6374
-9569,1534000,1980.3284
-9571,1536000,1967.7902
-9573,1538000,2029.6442
-9576,1540000,1388.4754
-9580,1542000,968.644
-9583,1544000,1086.4145
-9585,1546000,1970.0044
-9587,1548000,1975.1389
-9589,1550000,1993.5268
-9592,1552000,1438.3014
-9595,1554000,1177.714
-9597,1556000,1985.8237
-9600,1558000,1538.571
-9602,1560000,1694.8206
-9604,1562000,2006.0369
-9606,1564000,1960.6757
-9609,1566000,1355.732
-9612,1568000,1448.7594
-9615,1570000,1393.1051
-9617,1572000,1223.8063
-9619,1574000,2024.2306
-9622,1576000,1543.1495
-9625,1578000,1465.1281
-9628,1580000,1392.875
-9630,1582000,1942.6656
-9632,1584000,1859.7928
-9636,1586000,920.9009
-9639,1588000,781.9076
-9641,1590000,1962.6831
-9644,1592000,1879.3194
-9646,1594000,1526.7997
-9648,1596000,1924.1123
-9650,1598000,1961.3479
-9652,1600000,1984.3971
-9654,1602000,1945.1375
-9656,1604000,1769.6317
-9658,1606000,1988.0629
-9660,1608000,1896.0448
-9662,1610000,1842.4861
-9664,1612000,1965.8742
-9666,1614000,1925.4545
-9670,1616000,999.2186
-9673,1618000,1434.7425
-9676,1620000,1290.946
-9679,1622000,1601.781
-9681,1624000,1677.7835
-9683,1626000,1700.9637
-9685,1628000,1970.0581
-9688,1630000,1826.588
-9692,1632000,989.7568
-9695,1634000,992.4579
-9699,1636000,916.2182
-9702,1638000,1581.0068
-9705,1640000,1636.3605
-9708,1642000,1278.0895
-9710,1644000,2005.948
-9714,1646000,1023.194
-9717,1648000,1233.0104
-9720,1650000,909.5139
-9725,1652000,1023.7136
-9727,1654000,1873.2005
-9731,1656000,930.1775
-9734,1658000,1476.5547
-9736,1660000,1272.961
-9739,1662000,1548.6857
-9741,1664000,1914.8266
-9744,1666000,1448.2002
-9746,1668000,1734.5917
-9749,1670000,1335.5169
-9752,1672000,1388.4663
-9754,1674000,2014.0753
-9756,1676000,1995.6828
-9758,1678000,2015.0033
-9760,1680000,1996.457
-9763,1682000,1710.9856
-9765,1684000,1488.9285
-9768,1686000,1528.4909
-9770,1688000,1958.0489
-9773,1690000,1137.1877
-9777,1692000,1284.9579
-9780,1694000,1272.7794
-9784,1696000,994.2478
-9786,1698000,2042.4824
-9788,1700000,1982.3939
-9792,1702000,983.9846
-9795,1704000,1261.7283
-9798,1706000,1249.1583
-9801,1708000,1380.6805
-9804,1710000,1121.6272
-9806,1712000,1870.3853
-9808,1714000,1995.6697
-9811,1716000,1585.4936
-9813,1718000,1735.6418
-9816,1720000,1521.6399
-9818,1722000,1887.9015
-9820,1724000,1750.2311
-9822,1726000,1609.0668
-9824,1728000,1906.4708
-9826,1730000,1925.4121
-9829,1732000,1451.7099
-9831,1734000,1956.8796
-9834,1736000,1442.1458
-9836,1738000,1661.1206
-9838,1740000,1653.1771
-9841,1742000,1429.551
-9843,1744000,1988.3748
-9845,1746000,1748.7154
-9847,1748000,1793.4662
-9849,1750000,1914.8139
-9851,1752000,1952.731
-9853,1754000,1970.7327
-9856,1756000,1828.5495
-9858,1758000,2001.1613
-9860,1760000,1950.1297
-9862,1762000,1574.0358
-9864,1764000,1963.894
-9866,1766000,1985.43
-9868,1768000,1934.3661
-9870,1770000,1966.4271
-9872,1772000,2007.9808
-9874,1774000,1997.7661
-9876,1776000,1975.4293
-9878,1778000,1833.9522
-9880,1780000,1996.5475
-9882,1782000,2012.3804
-9884,1784000,2009.4794
-9886,1786000,2044.5144
-9888,1788000,1990.1106
-9890,1790000,1987.8763
-9892,1792000,2059.8658
-9894,1794000,2018.0624
-9896,1796000,1995.8234
-9898,1798000,2000.7645
-9900,1800000,1759.7932
-9903,1802000,1816.577
-9905,1804000,1307.8593
-9908,1806000,1678.7071
-9910,1808000,1976.3161
-9912,1810000,1994.2828
-9914,1812000,1983.1048
-9916,1814000,1998.252
-9918,1816000,2031.7793
-9920,1818000,2002.3153
-9922,1820000,1986.7777
-9924,1822000,1999.345
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-9928,1826000,2032.9691
-9930,1828000,1425.988
-9933,1830000,1753.8128
-9935,1832000,1991.9827
-9937,1834000,2024.3695
-9939,1836000,1405.9094
-9941,1838000,1996.158
-9943,1840000,2005.6762
-9945,1842000,1996.2393
-9947,1844000,2000.8182
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-9951,1848000,1996.2854
-9953,1850000,2043.6515
-9955,1852000,2040.0199
-9957,1854000,2016.4515
-9959,1856000,2046.2602
-9961,1858000,2024.7557
-9963,1860000,2029.2647
-9965,1862000,2031.867
-9967,1864000,2018.3083
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-9976,1872000,2010.6947
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-9984,1880000,2010.1299
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-9990,1886000,2028.5882
-9992,1888000,2020.8835
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-10000,1896000,2035.7585
-10002,1898000,2016.8506
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diff --git a/PPO_logs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_log_2.csv b/PPO_logs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_log_2.csv
deleted file mode 100644
index 00d598b..0000000
--- a/PPO_logs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_log_2.csv
+++ /dev/null
@@ -1,1501 +0,0 @@
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diff --git a/PPO_logs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_log_0.csv b/PPO_logs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_log_0.csv
deleted file mode 100644
index fb9e01f..0000000
--- a/PPO_logs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_log_0.csv
+++ /dev/null
@@ -1,1501 +0,0 @@
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diff --git a/PPO_logs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_log_1.csv b/PPO_logs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_log_1.csv
deleted file mode 100644
index f2074ce..0000000
--- a/PPO_logs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_log_1.csv
+++ /dev/null
@@ -1,1501 +0,0 @@
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diff --git a/PPO_logs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_log_2.csv b/PPO_logs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_log_2.csv
deleted file mode 100644
index a14b7fa..0000000
--- a/PPO_logs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_log_2.csv
+++ /dev/null
@@ -1,1501 +0,0 @@
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-9439,2998000,989.8245
-9441,3000000,984.8811
diff --git a/PPO_preTrained/PandaReachDense-v2/PPO_PandaReachDense-v2_0_0.pth b/PPO_preTrained/PandaReachDense-v2/PPO_PandaReachDense-v2_0_0.pth
new file mode 100644
index 0000000..fba2698
Binary files /dev/null and b/PPO_preTrained/PandaReachDense-v2/PPO_PandaReachDense-v2_0_0.pth differ
diff --git a/README.md b/README.md
index 515448a..cf1bb39 100644
--- a/README.md
+++ b/README.md
@@ -1,29 +1,19 @@
-# PPO-PyTorch
+# PPO-Panda
 
-### UPDATE [April 2021] : 
+### UPDATE [January 2022] : 
 
-- merged discrete and continuous algorithms
-- added linear decaying for the continuous action space `action_std`; to make training more stable for complex environments
-- added different learning rates for actor and critic
-- episodes, timesteps and rewards are now logged in `.csv` files
-- utils to plot graphs from log files
-- utils to test and make gifs from preTrained networks
-- `PPO_colab.ipynb` combining all the files to train / test / plot graphs / make gifs on google colab in a convenient jupyter-notebook
+- Modified [this commit](https://github.com/nikhilbarhate99/PPO-PyTorch) created by Nikhil Barhate to accomodate the Franka Emika Panda Robot.
+- Installed Panda-Gym
+- Adapted the classes to account for the difference in return after the step  
 
-#### [Open `PPO_colab.ipynb` in Google Colab](https://colab.research.google.com/github/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_colab.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_colab.ipynb)
+#### [Open `PPO_colab.ipynb` in Google Colab](https://colab.research.google.com/github/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_colab.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_colab.ipynb) to see original PPO implementation for roboschool
 
 
 ## Introduction
 
-This repository provides a Minimal PyTorch implementation of Proximal Policy Optimization (PPO) with clipped objective for OpenAI gym environments. It is primarily intended for beginners in [Reinforcement Learning](https://en.wikipedia.org/wiki/Reinforcement_learning) for understanding the PPO algorithm. It can still be used for complex environments but may require some hyperparameter-tuning or changes in the code.
-
-To keep the training procedure simple : 
-  - It has a **constant standard deviation** for the output action distribution (**multivariate normal with diagonal covariance matrix**) for the continuous environments, i.e. it is a hyperparameter and NOT a trainable parameter. However, it is **linearly decayed**. (action_std significantly affects performance)
-  - It uses simple **monte-carlo estimate** for calculating advantages and NOT Generalized Advantage Estimate (check out the OpenAI spinning up implementation for that).
-  - It is a **single threaded implementation**, i.e. only one worker collects experience. [One of the older forks](https://github.com/rhklite/Parallel-PPO-PyTorch) of this repository has been modified to have Parallel workers
-
-A concise explaination of PPO algorithm can be found [here](https://stackoverflow.com/questions/46422845/what-is-the-way-to-understand-proximal-policy-optimization-algorithm-in-rl)
-
+- See [this link](https://github.com/nikhilbarhate99/PPO-PyTorch) to review the details (learning rates, episode logging, utils, etc) of this implementation of PPO. 
+- New modifications include to the reward figure, the rendering, etc
+- No changes in PPO
 
 ## Usage
 
@@ -36,51 +26,26 @@ A concise explaination of PPO algorithm can be found [here](https://stackoverflo
 - All the **hyperparameters used for training (preTrained) policies are listed** in the [`README.md` in PPO_preTrained directory](https://github.com/nikhilbarhate99/PPO-PyTorch/tree/master/PPO_preTrained)
 
 #### Note :
-  - if the environment runs on CPU, use CPU as device for faster training. Box-2d and Roboschool run on CPU and training them on GPU device will be significantly slower because the data will be moved between CPU and GPU often
-
+  - if the environment runs on CPU, use CPU as device for faster training. 
+  
 ## Citing 
 
 Please use this bibtex if you want to cite this repository in your publications :
 
-    @misc{pytorch_minimal_ppo,
-        author = {Barhate, Nikhil},
-        title = {Minimal PyTorch Implementation of Proximal Policy Optimization},
-        year = {2021},
+    @misc{ppo_panda,
+        author = {Lobbezoo, Andrew},
+        title = {PyTorch Implementation of Proximal Policy Optimization for the OpenAI Panda},
+        year = {2022},
         publisher = {GitHub},
         journal = {GitHub repository},
-        howpublished = {\url{https://github.com/nikhilbarhate99/PPO-PyTorch}},
+        howpublished = {\url{https://github.com/alobbezoo/PPO-Panda}},
     }
 
 ## Results
 
-| PPO Continuous RoboschoolHalfCheetah-v1  | PPO Continuous RoboschoolHalfCheetah-v1 |
+| PPO Continuous PandaReachDense-v2  | PPO Continuous PandaReachDense-v2 |
 | :-------------------------:|:-------------------------: |
-| ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_gifs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_gif_0.gif) |  ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_figs/RoboschoolHalfCheetah-v1/PPO_RoboschoolHalfCheetah-v1_fig_0.png) |
-
-
-| PPO Continuous RoboschoolHopper-v1  | PPO Continuous RoboschoolHopper-v1 |
-| :-------------------------:|:-------------------------: |
-| ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_gifs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_gif_0.gif) |  ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_figs/RoboschoolHopper-v1/PPO_RoboschoolHopper-v1_fig_0.png) |
-
-
-| PPO Continuous RoboschoolWalker2d-v1  | PPO Continuous RoboschoolWalker2d-v1 |
-| :-------------------------:|:-------------------------: |
-| ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_gifs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_gif_0.gif) |  ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_figs/RoboschoolWalker2d-v1/PPO_RoboschoolWalker2d-v1_fig_0.png) |
-
-
-| PPO Continuous BipedalWalker-v2  | PPO Continuous BipedalWalker-v2 |
-| :-------------------------:|:-------------------------: |
-| ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_gifs/BipedalWalker-v2/PPO_BipedalWalker-v2_gif_0.gif) |  ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_figs/BipedalWalker-v2/PPO_BipedalWalker-v2_fig_0.png) |
-
-
-| PPO Discrete CartPole-v1  | PPO Discrete CartPole-v1 |
-| :-------------------------:|:-------------------------: |
-| ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_gifs/CartPole-v1/PPO_CartPole-v1_gif_0.gif) |  ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_figs/CartPole-v1/PPO_CartPole-v1_fig_0.png) |
-
-
-| PPO Discrete LunarLander-v2  | PPO Discrete LunarLander-v2 |
-| :-------------------------:|:-------------------------: |
-| ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_gifs/LunarLander-v2/PPO_LunarLander-v2_gif_0.gif) |  ![](https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_figs/LunarLander-v2/PPO_LunarLander-v2_fig_0.png) |
+| ![](https://github.com/alobbezoo/PPO-Panda/blob/b470118413237fd8d52055cb9880a3b5dfa17040/PPO_gifs/PandaReachDense-v2/PPO_PandaReachDense-v2_gif_0.gif) |  ![](https://github.com/alobbezoo/PPO-Panda/blob/master/PPO_figs/PandaReachDense-v2/PPO_PandaReachDense-v2_fig_0.png) |
 
 
 ## Dependencies
@@ -93,15 +58,16 @@ gym
 ```
 Training Environments 
 ```
-Box-2d
-Roboschool
-pybullet
+gym
 ```
 Graphs and gifs
 ```
 pandas
 matplotlib
 Pillow
+pyvirtualdisplay
+python-opengl
+
 ```
 
 
@@ -109,5 +75,6 @@ Pillow
 
 - [PPO paper](https://arxiv.org/abs/1707.06347)
 - [OpenAI Spinning up](https://spinningup.openai.com/en/latest/)
+- [PPO-PyTorch](https://github.com/nikhilbarhate99/PPO-PyTorch)
 
 
diff --git a/__pycache__/PPO.cpython-38.pyc b/__pycache__/PPO.cpython-38.pyc
new file mode 100644
index 0000000..4dcbe20
Binary files /dev/null and b/__pycache__/PPO.cpython-38.pyc differ
diff --git a/make_gif.py b/make_gif.py
index c3839b7..1e7a747 100644
--- a/make_gif.py
+++ b/make_gif.py
@@ -8,9 +8,8 @@
 from PIL import Image
 
 import gym
-import roboschool
-
-# import pybullet_envs
+import panda_gym
+import pybullet
 
 from PPO import PPO
 
@@ -40,7 +39,7 @@ def save_gif_images(env_name, has_continuous_action_space, max_ep_len, action_st
 
 	print("============================================================================================")
 
-	total_test_episodes = 1     # save gif for only one episode
+	total_test_episodes = 10     # save gif for only one episode
 
 
 	K_epochs = 80               # update policy for K epochs
@@ -51,10 +50,10 @@ def save_gif_images(env_name, has_continuous_action_space, max_ep_len, action_st
 	lr_critic = 0.001         # learning rate for critic
 
 
-	env = gym.make(env_name)
+	env = gym.make(env_name, render=True)
 
 	# state space dimension
-	state_dim = env.observation_space.shape[0]
+	state_dim = 6 #env.observation_space.shape[0]
 
 	# action space dimension
 	if has_continuous_action_space:
@@ -110,20 +109,29 @@ def save_gif_images(env_name, has_continuous_action_space, max_ep_len, action_st
 	for ep in range(1, total_test_episodes+1):
 
 		ep_reward = 0
-		state = env.reset()
+		# state = env.reset()
+		state_dict = env.reset()
+		state = np.concatenate((state_dict['observation'][0:3], state_dict['desired_goal']))
 
 		for t in range(1, max_ep_len+1):
-		    action = ppo_agent.select_action(state)
-		    state, reward, done, _ = env.step(action)
-		    ep_reward += reward
+			action = ppo_agent.select_action(state)
+			next_state_dict, reward, done, _ = env.step(action)
+			state = np.concatenate((next_state_dict['observation'][0:3], next_state_dict['desired_goal']))
+			# action = ppo_agent.select_action(state)
+			# state, reward, done, _ = env.step(action)
+			print(reward)
+			ep_reward += reward
+			img = env.render(mode = 'rgb_array')
+
+			img = Image.fromarray(img)
 
-		    img = env.render(mode = 'rgb_array')
+			img = img.convert('RGB')
+			# new line to convert the type
 
-		    img = Image.fromarray(img)
-		    img.save(gif_images_dir + '/' + str(t).zfill(6) + '.jpg')
+			img.save(gif_images_dir + '/' + str(t).zfill(6) + '.jpg')
 
-		    if done:
-		        break
+			if done:
+				break
 
 		# clear buffer
 		ppo_agent.buffer.clear()
@@ -234,41 +242,11 @@ def list_gif_size(env_name):
 if __name__ == '__main__':
 
 
-	# env_name = "CartPole-v1"
-	# has_continuous_action_space = False
-	# max_ep_len = 400
-	# action_std = None
-
-
-	# env_name = "LunarLander-v2"
-	# has_continuous_action_space = False
-	# max_ep_len = 500
-	# action_std = None
-
-
-	# env_name = "BipedalWalker-v2"
-	# has_continuous_action_space = True
-	# max_ep_len = 1500           # max timesteps in one episode
-	# action_std = 0.1            # set same std for action distribution which was used while saving
-
-
-	# env_name = "RoboschoolWalker2d-v1"
-	# has_continuous_action_space = True
-	# max_ep_len = 1000           # max timesteps in one episode
-	# action_std = 0.1            # set same std for action distribution which was used while saving
-
-
-	env_name = "RoboschoolHalfCheetah-v1"
+	env_name = "PandaReachDense-v2"
 	has_continuous_action_space = True
 	max_ep_len = 1000           # max timesteps in one episode
 	action_std = 0.1            # set same std for action distribution which was used while saving
 
-
-	# env_name = "RoboschoolHopper-v1"
-	# has_continuous_action_space = True
-	# max_ep_len = 1000           # max timesteps in one episode
-	# action_std = 0.1            # set same std for action distribution which was used while saving
-
 	# save .jpg images in PPO_gif_images folder
 	save_gif_images(env_name, has_continuous_action_space, max_ep_len, action_std)
 
diff --git a/plot_graph.py b/plot_graph.py
index d058497..0c7f84a 100644
--- a/plot_graph.py
+++ b/plot_graph.py
@@ -7,10 +7,7 @@ def save_graph():
 
     print("============================================================================================")
 
-    # env_name = 'CartPole-v1'
-    # env_name = 'LunarLander-v2'
-    # env_name = 'BipedalWalker-v2'
-    env_name = 'RoboschoolWalker2d-v1'
+    env_name = "PandaReachDense-v2"
 
 
     fig_num = 0     #### change this to prevent overwriting figures in same env_name folder
@@ -22,7 +19,7 @@ def save_graph():
 
 
     # smooth out rewards to get a smooth and a less smooth (var) plot lines
-    window_len_smooth = 20
+    window_len_smooth = 50
     min_window_len_smooth = 1
     linewidth_smooth = 1.5
     alpha_smooth = 1
@@ -84,7 +81,7 @@ def save_graph():
         data_avg['reward_smooth'] = data_avg['reward'].rolling(window=window_len_smooth, win_type='triang', min_periods=min_window_len_smooth).mean()
         data_avg['reward_var'] = data_avg['reward'].rolling(window=window_len_var, win_type='triang', min_periods=min_window_len_var).mean()
 
-        data_avg.plot(kind='line', x='timestep' , y='reward_smooth',ax=ax,color=colors[0],  linewidth=linewidth_smooth, alpha=alpha_smooth)
+        # data_avg.plot(kind='line', x='timestep' , y='reward_smooth',ax=ax,color=colors[0],  linewidth=linewidth_smooth, alpha=alpha_smooth)
         data_avg.plot(kind='line', x='timestep' , y='reward_var',ax=ax,color=colors[0],  linewidth=linewidth_var, alpha=alpha_var)
 
         # keep only reward_smooth in the legend and rename it
@@ -100,6 +97,8 @@ def save_graph():
 
             # plot the lines
             run.plot(kind='line', x='timestep' , y='reward_smooth_' + str(i),ax=ax,color=colors[i % len(colors)],  linewidth=linewidth_smooth, alpha=alpha_smooth)
+            # not interested in avg
+
             run.plot(kind='line', x='timestep' , y='reward_var_' + str(i),ax=ax,color=colors[i % len(colors)],  linewidth=linewidth_var, alpha=alpha_var)
 
         # keep alternate elements (reward_smooth_i) in the legend
diff --git a/test.py b/test.py
index 812fc12..c8a1f05 100644
--- a/test.py
+++ b/test.py
@@ -4,12 +4,16 @@
 from datetime import datetime
 
 import torch
+import torch.nn as nn
+from torch.distributions import MultivariateNormal
+from torch.distributions import Categorical
+
 import numpy as np
 
 import gym
-import roboschool
+import panda_gym
+import pybullet
 
-# import pybullet_envs
 
 from PPO import PPO
 
@@ -24,31 +28,12 @@ def test():
 
     ################## hyperparameters ##################
 
-    # env_name = "CartPole-v1"
-    # has_continuous_action_space = False
-    # max_ep_len = 400
-    # action_std = None
-
-
-    # env_name = "LunarLander-v2"
-    # has_continuous_action_space = False
-    # max_ep_len = 300
-    # action_std = None
-
-
-    # env_name = "BipedalWalker-v2"
-    # has_continuous_action_space = True
-    # max_ep_len = 1500           # max timesteps in one episode
-    # action_std = 0.1            # set same std for action distribution which was used while saving
-
-
-    env_name = "RoboschoolWalker2d-v1"
+    env_name = "PandaReachDense-v2"
     has_continuous_action_space = True
-    max_ep_len = 1000           # max timesteps in one episode
+    max_ep_len = 500           # max timesteps in one episode
     action_std = 0.1            # set same std for action distribution which was used while saving
 
-
-    render = True              # render environment on screen
+    render = False #True        # render environment on screen, false is faster
     frame_delay = 0             # if required; add delay b/w frames
 
 
@@ -67,9 +52,10 @@ def test():
     env = gym.make(env_name)
 
     # state space dimension
-    state_dim = env.observation_space.shape[0]
-
+    state_dim = 6 #env.observation_space.shape[0]
+    # we know that the state will have 6 elements, xyz of target and gripper (3+3)
     # action space dimension
+
     if has_continuous_action_space:
         action_dim = env.action_space.shape[0]
     else:
@@ -100,11 +86,16 @@ def test():
 
     for ep in range(1, total_test_episodes+1):
         ep_reward = 0
-        state = env.reset()
+        # state = env.reset()
+        next_state_dict = env.reset()
+        state = np.concatenate((next_state_dict['observation'][0:3], next_state_dict['desired_goal']))
 
         for t in range(1, max_ep_len+1):
             action = ppo_agent.select_action(state)
-            state, reward, done, _ = env.step(action)
+            next_state_dict, reward, done, _ = env.step(action)
+            state = np.concatenate((next_state_dict['observation'][0:3], next_state_dict['desired_goal']))
+            # state, reward, done, _ = env.step(action)
+
             ep_reward += reward
 
             if render:
diff --git a/train.py b/train.py
index a5b04a2..6fbcb9e 100644
--- a/train.py
+++ b/train.py
@@ -4,12 +4,18 @@
 from datetime import datetime
 
 import torch
+import torch.nn as nn
+from torch.distributions import MultivariateNormal
+from torch.distributions import Categorical
+
 import numpy as np
 
 import gym
-import roboschool
+import panda_gym
+import pybullet
 
-# import pybullet_envs
+# import roboschool
+#import pybullet_envs
 
 from PPO import PPO
 
@@ -24,16 +30,16 @@ def train():
 
     ####### initialize environment hyperparameters ######
 
-    env_name = "RoboschoolWalker2d-v1"
+    env_name = "PandaReachDense-v2"
 
     has_continuous_action_space = True  # continuous action space; else discrete
 
-    max_ep_len = 1000                   # max timesteps in one episode
-    max_training_timesteps = int(3e6)   # break training loop if timeteps > max_training_timesteps
+    max_ep_len = 400                   # max timesteps in one episode
+    max_training_timesteps = int(1e5)   # break training loop if timeteps > max_training_timesteps
 
-    print_freq = max_ep_len * 10        # print avg reward in the interval (in num timesteps)
+    print_freq = max_ep_len * 4        # print avg reward in the interval (in num timesteps)
     log_freq = max_ep_len * 2           # log avg reward in the interval (in num timesteps)
-    save_model_freq = int(1e5)          # save model frequency (in num timesteps)
+    save_model_freq = int(2e4)          # save model frequency (in num timesteps)
 
     action_std = 0.6                    # starting std for action distribution (Multivariate Normal)
     action_std_decay_rate = 0.05        # linearly decay action_std (action_std = action_std - action_std_decay_rate)
@@ -68,7 +74,8 @@ def train():
     env = gym.make(env_name)
 
     # state space dimension
-    state_dim = env.observation_space.shape[0]
+    state_dim = 6 #env.observation_space.shape[0]
+    # we know that the state will have 6 elements, xyz of target and gripper (3+3)
 
     # action space dimension
     if has_continuous_action_space:
@@ -209,14 +216,21 @@ def train():
     # training loop
     while time_step <= max_training_timesteps:
 
-        state = env.reset()
+        # state = env.reset()
+        state_dict = env.reset()
+        state = state_dict["observation"]
+
         current_ep_reward = 0
 
         for t in range(1, max_ep_len+1):
 
             # select action with policy
             action = ppo_agent.select_action(state)
-            state, reward, done, _ = env.step(action)
+            # state, reward, done, _ = env.step(action)
+            next_state_dict, reward, done, _ = env.step(action)
+            state = np.concatenate((next_state_dict['observation'][0:3], next_state_dict['desired_goal']))
+            # NOTE: the next_state_dict is actually a dictionary which stores several different observations
+            # concatenate the key observations for training here
 
             # saving reward and is_terminals
             ppo_agent.buffer.rewards.append(reward)