From 82a4c2325883bb058a170a7a76e4c755f6a1f50e Mon Sep 17 00:00:00 2001
From: Tobi <tobias.hassel1@web.de>
Date: Sun, 19 May 2019 21:18:13 +0200
Subject: [PATCH] Training now works / bug memory error while training

---
 Assets/Brains/CozmoLearningBrain.asset |     5 +-
 Assets/Scripts/ML Cozmo/CozmoAgent.cs  |    19 +-
 Notebooks/UnitySDK.log                 | 15742 +++++++++++++++++++++++
 Notebooks/getting-started.ipynb        |  1039 +-
 4 files changed, 15774 insertions(+), 1031 deletions(-)

diff --git a/Assets/Brains/CozmoLearningBrain.asset b/Assets/Brains/CozmoLearningBrain.asset
index 908c86d..042ab83 100644
--- a/Assets/Brains/CozmoLearningBrain.asset
+++ b/Assets/Brains/CozmoLearningBrain.asset
@@ -15,14 +15,13 @@ MonoBehaviour:
   brainParameters:
     vectorObservationSize: 0
     numStackedVectorObservations: 1
-    vectorActionSize: 0200000002000000
+    vectorActionSize: 04000000
     cameraResolutions:
     - width: 320
       height: 240
       blackAndWhite: 0
     vectorActionDescriptions:
-    - forward
-    - rotation
+    - 
     vectorActionSpaceType: 0
   model: {fileID: 0}
   inferenceDevice: 0
diff --git a/Assets/Scripts/ML Cozmo/CozmoAgent.cs b/Assets/Scripts/ML Cozmo/CozmoAgent.cs
index d4f48df..8c48ab7 100644
--- a/Assets/Scripts/ML Cozmo/CozmoAgent.cs	
+++ b/Assets/Scripts/ML Cozmo/CozmoAgent.cs	
@@ -51,10 +51,12 @@ public class CozmoAgent : Agent
     // to be implemented by the developer
     public override void AgentAction(float[] vectorAction, string textAction)
     {
-
+        //print("Action before FloorToInt: " + vectorAction[0]);
         int action = Mathf.FloorToInt(vectorAction[0]);
         Point centerOfGravity = onRenderImageTest.CenterOfGravity;
-        Vector3 targetPos = transform.position;
+        //Vector3 targetPos = transform.position;
+
+        //print("Action after FloorToInt: " + action);
 
         AddReward(-0.01f);
 
@@ -73,7 +75,7 @@ public class CozmoAgent : Agent
                 movement.Turn(-1);
                 break;
             default:
-                movement.Move(0);
+                //movement.Move(0);
                 throw new ArgumentException("Invalid action value. Stop movement.");
         }
 
@@ -87,21 +89,18 @@ public class CozmoAgent : Agent
         // If centerOfGravity lies near to the center of the image horizontally
         if (centerOfGravity.X > renderCamera.targetTexture.width / 2 - nearAreaLimit && centerOfGravity.X < renderCamera.targetTexture.width / 2 + nearAreaLimit)
         {
-            Done();
             SetReward(1);
             print("Reward: +1");
         }
         else if (centerOfGravity.X > renderCamera.targetTexture.width / 2 - farAreaLimit && centerOfGravity.X < renderCamera.targetTexture.width / 2 + farAreaLimit)
         {
-            Done();
-            SetReward(-1);
-            print("Reward: -1");
+            SetReward(-0.7f);
+            print("Reward: -0.7");
         }
         else
         {
-            Done();
-            SetReward(-2);
-            print("Reward: -2");
+            SetReward(-1);
+            print("Reward: -1");
         }
     }
 
diff --git a/Notebooks/UnitySDK.log b/Notebooks/UnitySDK.log
index 35d1d2b..6bb5be1 100644
--- a/Notebooks/UnitySDK.log
+++ b/Notebooks/UnitySDK.log
@@ -488721,3 +488721,15745 @@ Reward: +1
 Log
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diff --git a/Notebooks/getting-started.ipynb b/Notebooks/getting-started.ipynb
index c6c43d3..422734e 100644
--- a/Notebooks/getting-started.ipynb
+++ b/Notebooks/getting-started.ipynb
@@ -99,8 +99,8 @@
       "        Vector Observation space size (per agent): 0\n",
       "        Number of stacked Vector Observation: 1\n",
       "        Vector Action space type: discrete\n",
-      "        Vector Action space size (per agent): [2, 2]\n",
-      "        Vector Action descriptions: forward, rotation\n"
+      "        Vector Action space size (per agent): [4]\n",
+      "        Vector Action descriptions: \n"
      ]
     }
    ],
@@ -195,1029 +195,32 @@
      "name": "stdout",
      "output_type": "stream",
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+     "ename": "MemoryError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-3140409a1130>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m             \u001b[0maction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn_stack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction_size\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv_info\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magents\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m             \u001b[0menv_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdefault_brain\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m         \u001b[0mepisode_rewards\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0menv_info\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrewards\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[0mdone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv_info\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocal_done\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\environment.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, vector_action, memory, text_action, value, custom_action)\u001b[0m\n\u001b[0;32m    436\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    437\u001b[0m             \u001b[0mrl_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrl_output\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 438\u001b[1;33m             \u001b[0mstate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrl_output\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    439\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_global_done\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    440\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0m_b\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_external_brain_names\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\environment.py\u001b[0m in \u001b[0;36m_get_state\u001b[1;34m(self, output)\u001b[0m\n\u001b[0;32m    496\u001b[0m             \u001b[0magent_info_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magentInfos\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mbrain_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\u001b[0m             _data[brain_name] = BrainInfo.from_agent_proto(agent_info_list,\n\u001b[1;32m--> 498\u001b[1;33m                                                            self.brains[brain_name])\n\u001b[0m\u001b[0;32m    499\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mglobal_done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\brain.py\u001b[0m in \u001b[0;36mfrom_agent_proto\u001b[1;34m(agent_info_list, brain_params)\u001b[0m\n\u001b[0;32m     91\u001b[0m             obs = [BrainInfo.process_pixels(x.visual_observations[i],\n\u001b[0;32m     92\u001b[0m                                             brain_params.camera_resolutions[i]['blackAndWhite'])\n\u001b[1;32m---> 93\u001b[1;33m                    for x in agent_info_list]\n\u001b[0m\u001b[0;32m     94\u001b[0m             \u001b[0mvis_obs\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magent_info_list\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\brain.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     91\u001b[0m             obs = [BrainInfo.process_pixels(x.visual_observations[i],\n\u001b[0;32m     92\u001b[0m                                             brain_params.camera_resolutions[i]['blackAndWhite'])\n\u001b[1;32m---> 93\u001b[1;33m                    for x in agent_info_list]\n\u001b[0m\u001b[0;32m     94\u001b[0m             \u001b[0mvis_obs\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     95\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magent_info_list\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\tobi\\downloads\\ml-agents-0.8.1\\ml-agents-envs\\mlagents\\envs\\brain.py\u001b[0m in \u001b[0;36mprocess_pixels\u001b[1;34m(image_bytes, gray_scale)\u001b[0m\n\u001b[0;32m     76\u001b[0m         \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage_bytes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m         \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m         \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m255.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     79\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mgray_scale\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m             \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mMemoryError\u001b[0m: "
      ]
     }
    ],
    "source": [
-    "for episode in range(1000):\n",
+    "for episode in range(100):\n",
     "    env_info = env.reset(train_mode=train_mode)[default_brain]\n",
     "    done = False\n",
     "    episode_rewards = 0\n",