From 89dd3132b44c1ec51f4ffac7bf6014d4cbe31dfb Mon Sep 17 00:00:00 2001
From: hagrid67 <jdhwatson@gmail.com>
Date: Thu, 1 Oct 2020 19:01:20 +0100
Subject: [PATCH] fixed service.py to work with test-service notebook.  Also
 made it send errors with pickle when pickle is set

---
 flatland/evaluators/service.py |   31 +-
 notebooks/test-service.ipynb   | 1324 ++------------------------------
 2 files changed, 76 insertions(+), 1279 deletions(-)

diff --git a/flatland/evaluators/service.py b/flatland/evaluators/service.py
index 7ca6f39a..edffc419 100644
--- a/flatland/evaluators/service.py
+++ b/flatland/evaluators/service.py
@@ -642,8 +642,15 @@ class FlatlandRemoteEvaluationService:
         # reset the timeout flag / state.
         self.state_env_timed_out = False
 
-        test_env_file_path = self.env_file_paths[self.simulation_count]
-        env_test, env_level = self.get_env_test_and_level(test_env_file_path)
+        # Check if we have finished all the available envs
+        if self.simulation_count >= len(self.env_file_paths):
+            self.evaluation_done = True
+            # Hack - just ensure these are set
+            test_env_file_path = self.env_file_paths[self.simulation_count-1]
+            env_test, env_level = self.get_env_test_and_level(test_env_file_path)
+        else:
+            test_env_file_path = self.env_file_paths[self.simulation_count]
+            env_test, env_level = self.get_env_test_and_level(test_env_file_path)
 
         # Did we just finish a test, and if yes did it reach high enough mean percentage done?
         if self.current_test != env_test and env_test != 0:
@@ -679,12 +686,8 @@ class FlatlandRemoteEvaluationService:
             self.current_level = env_level
 
             del self.env
-            self.env = RailEnv(width=1, height=1,
-                               rail_generator=rail_from_file(test_env_file_path),
-                               schedule_generator=schedule_from_file(test_env_file_path),
-                               malfunction_generator_and_process_data=malfunction_from_file(test_env_file_path),
-                               obs_builder_object=DummyObservationBuilder(),
-                               record_steps=True)
+
+            self.env, _env_dict = RailEnvPersister.load_new(test_env_file_path)
 
             self.begin_simulation = time.time()
 
@@ -1101,13 +1104,17 @@ class FlatlandRemoteEvaluationService:
         _command_response = {}
         _command_response['type'] = messages.FLATLAND_RL.ERROR
         _command_response['payload'] = error_message
-        _redis.rpush(
-            command_response_channel,
-            msgpack.packb(
+
+        if self.use_pickle:
+            bytes_error = pickle.dumps(_command_response)
+        else:
+            bytes_error = msgpack.packb(
                 _command_response,
                 default=m.encode,
                 use_bin_type=True)
-        )
+
+        _redis.rpush(command_response_channel, bytes_error)
+
         self.evaluation_state["state"] = "ERROR"
         self.evaluation_state["error"] = error_message
         self.evaluation_state["meta"]["termination_cause"] = "An error occured."
diff --git a/notebooks/test-service.ipynb b/notebooks/test-service.ipynb
index 969e8420..807ee506 100644
--- a/notebooks/test-service.ipynb
+++ b/notebooks/test-service.ipynb
@@ -87,49 +87,9 @@
     "display.display(display.HTML(\"<style>.container { width:95% !important; }</style>\"))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "UeX1h4c0i5e6"
-   },
-   "source": [
-    "# Experiments\n",
-    "\n",
-    "This has been mostly changed to load envs using `importlib_resources`.  It's getting them from the package \"envdata.tests`"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "kAiCO4x75Rbw"
-   },
-   "outputs": [],
-   "source": [
-    "# ENV FILE PATH\n",
-    "#env_file = \"Test_20_Level_0.pkl\"\n",
-    "#env_file = \"../../evaluation_visualization/round2/or-0827/Test_23/Level_1.pkl\"\n",
-    "#env_file = \"../../evaluation_visualization/round2/rl-0827/Test_23/Level_1.pkl\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#if os.path.exists(\"../env_data\"):\n",
-    "#    env_file = \"../env_data/tests/Test_2_Level_0.pkl\"\n",
-    "#else:\n",
-    "#    env_file = \"./env_data/tests/Test_2_Level_0.pkl\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,17 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#env_file = \"../../evaluation_visualization/round2/or-0827/Test_23/Level_1.pkl\"\n",
-    "#env_file = \"../../evaluation_visualization/round2/rl-0827/Test_23/Level_1.pkl\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -170,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -208,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -226,7 +176,7 @@
        "[]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -238,12 +188,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "for sKey in lKeys:\n",
-    "    print(sKey)\n",
+    "    print(\"Deleting:\", sKey)\n",
     "    oRedis.delete(sKey)"
    ]
   },
@@ -257,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,18 +216,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
     "#sCmd = \"python -m flatland.evaluators.service --test_folder ../env_data/tests/service_test --mergeDir ./tmp/merge --actionDir ./tmp/actions --pickle --missingOnly\"\n",
-    "sCmd = \"python -m flatland.evaluators.service --test_folder ../env_data/tests/service_test --pickle --verbose\"\n",
+    "sCmd = \"python -m flatland.evaluators.service --test_folder ../env_data/tests/service_test --pickle\" # --verbose\"\n",
     "lsCmd = shlex.split(sCmd)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -287,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -296,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -305,7 +255,7 @@
        "subprocess.Popen"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -316,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -325,25 +275,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Request :  {'type': 'FLATLAND_RL.PING', 'payload': {'version': '2.2.1'}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::c26d0b28a6e1a7c59f3e022fa56a8b77', 'timestamp': 1601382710.231949}\n",
-      "Response :  b'\\x80\\x03}q\\x00(X\\x04\\x00\\x00\\x00typeq\\x01X\\x10\\x00\\x00\\x00FLATLAND_RL.PONGq\\x02X\\x07\\x00\\x00\\x00payloadq\\x03}q\\x04u.'\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "oFRC = FlatlandRemoteClient(test_envs_root=\"../env_data/tests/service_test/\", verbose=True, use_pickle=True)"
+    "oFRC = FlatlandRemoteClient(test_envs_root=\"../env_data/tests/service_test/\", verbose=False, use_pickle=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -353,21 +294,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def my_controller(obs, _env):\n",
-    "    dAct = ldActions[_env._elapsed_steps]\n",
-    "    #_action = {}\n",
-    "    #for _idx, _ in enumerate(_env.agents):\n",
-    "    #    _action[_idx] = np.random.randint(0, 5)\n",
+    "def expert_controller(obs, _env):\n",
+    "    return ldActions[_env._elapsed_steps]\n",
+    "\n",
+    "def random_controller(obs, _env):\n",
+    "    dAct = {}\n",
+    "    for iAg in range(len(_env.agents)):\n",
+    "        dAct[iAg] = np.random.randint(0, 5)\n",
     "    return dAct"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -376,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -385,7 +328,7 @@
        "True"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "metadata": {
     "scrolled": false
    },
@@ -405,769 +348,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Request :  {'type': 'FLATLAND_RL.ENV_CREATE', 'payload': {}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::c5f9c9736b4a07080d62491b55587291', 'timestamp': 1601382711.3886104}\n",
-      "Response :  b'\\x80\\x03}q\\x00(X\\x04\\x00\\x00\\x00typeq\\x01X\\x1f\\x00\\x00\\x00FLATLAND_RL.ENV_CREATE_RESPONSEq\\x02X\\x07\\x00\\x00\\x00payloadq\\x03}q\\x04(X\\x0b\\x00\\x00\\x00observationq\\x05\\x88X\\r\\x00\\x00\\x00env_file_pathq\\x06X\\x12\\x00\\x00\\x00Test_0/Level_0.pklq\\x07X\\x04\\x00\\x00\\x00infoq\\x08}q\\t(X\\x0f\\x00\\x00\\x00action_requiredq\\n}q\\x0b(K\\x00\\x88K\\x01\\x88K\\x02\\x88K\\x03\\x88K\\x04\\x88K\\x05\\x88K\\x06\\x88K\\x07\\x88K\\x08\\x88K\\t\\x88uX\\x0b\\x00\\x00\\x00malfunctionq\\x0c}q\\r(K\\x00K\\x00K\\x01K\\x00K\\x02K\\x00K\\x03K\\x00K\\x04K\\x00K\\x05K\\x00K\\x06K\\x00K\\x07K\\x00K\\x08K\\x00K\\tK\\x00uX\\x05\\x00\\x00\\x00speedq\\x0e}q\\x0f(K\\x00G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x01G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x02G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x03G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x04G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x05G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x06G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x07G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x08G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\tG?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00uX\\x06\\x00\\x00\\x00statusq\\x10}q\\x11(K\\x00cflatland.envs.agent_utils\\nRailAgentStatus\\nq\\x12K\\x00\\x85q\\x13Rq\\x14K\\x01h\\x14K\\x02h\\x14K\\x03h\\x14K\\x04h\\x14K\\x05h\\x14K\\x06h\\x14K\\x07h\\x14K\\x08h\\x14K\\th\\x14uuX\\x0b\\x00\\x00\\x00random_seedq\\x15M\\xe9\\x03uu.'\n",
-      "Received Env :  Test_0/Level_0.pkl\n",
-      "Current env path :  ../env_data/tests/service_test/Test_0/Level_0.pkl\n",
       "DEPRECATED - use FileMalfunctionGen instead of malfunction_from_file\n",
       "DEPRECATED - RailEnv arg: malfunction_and_process_data - use malfunction_generator\n",
       "Episode : 0\n",
       "False\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 2}, 'inference_time': 7.510185241699219e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::fcbc1a592db4b9ab83ee4b82489be98d', 'timestamp': 1601382711.4002197}\n",
-      "Step Time :  0.0022978782653808594\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 2}, 'inference_time': 7.724761962890625e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::01660f051b3f375a8a00683810f55df6', 'timestamp': 1601382711.4025884}\n",
-      "Step Time :  0.003253936767578125\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 1, 3: 2, 8: 4}, 'inference_time': 0.0001881122589111328}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::8f8a0507d4ce4aaff58d3754c2470535', 'timestamp': 1601382711.4060314}\n",
-      "Step Time :  0.0032224655151367188\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 4, 3: 4, 8: 1}, 'inference_time': 8.392333984375e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::dcbe785e0485cdea9eea2635cf50ff2e', 'timestamp': 1601382711.4093235}\n",
-      "Step Time :  0.0022280216217041016\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 3, 3: 1, 8: 3}, 'inference_time': 9.489059448242188e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::84fe3e3f27788eafc3e651a0b2dc6381', 'timestamp': 1601382711.4116464}\n",
-      "Step Time :  0.0026159286499023438\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 3, 8: 4}, 'inference_time': 0.00020122528076171875}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::c6c940aa9ecedb1148ee20bb3fdb4137', 'timestamp': 1601382711.414452}\n",
-      "Step Time :  0.0015065670013427734\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 1}, 'inference_time': 5.6743621826171875e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::b31263b6c34f9fae6a601b49dcb269cf', 'timestamp': 1601382711.4160109}\n",
-      "Step Time :  0.0012655258178710938\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 4, 8: 4}, 'inference_time': 4.029273986816406e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::989cb1bec7ffe8eb46654f97c9c5c3d8', 'timestamp': 1601382711.4173107}\n",
-      "Step Time :  0.0013382434844970703\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 3, 3: 2, 8: 4}, 'inference_time': 0.0005276203155517578}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::5953731bab617e774304306fe227cb76', 'timestamp': 1601382711.41918}\n",
-      "Step Time :  0.003258943557739258\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 3}, 'inference_time': 0.0005054473876953125}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::7a5baaa32fae1f442e5d5097e8e7bd61', 'timestamp': 1601382711.422997}\n",
-      "Step Time :  0.002592802047729492\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 3, 8: 4}, 'inference_time': 0.00022459030151367188}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::9233d209716f4ae78a5dbe124de67e27', 'timestamp': 1601382711.4257793}\n",
-      "Step Time :  0.0035974979400634766\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 3}, 'inference_time': 8.940696716308594e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::86a6797d4a4dc741a0d88243b124638c', 'timestamp': 1601382711.4294176}\n",
-      "Step Time :  0.0015513896942138672\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {3: 2, 1: 2, 8: 4}, 'inference_time': 5.1021575927734375e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::5d70186b357a8e3b6e47f4b7d75e8c25', 'timestamp': 1601382711.431004}\n",
-      "Step Time :  0.0012707710266113281\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {3: 2, 1: 4, 8: 4}, 'inference_time': 3.838539123535156e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::2872c8d42231e56eba7350e8ee58fb0f', 'timestamp': 1601382711.4323094}\n",
-      "Step Time :  0.0012013912200927734\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 4, 3: 4, 8: 4}, 'inference_time': 3.695487976074219e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::d1575976e20855eeb73a94ec8b35704c', 'timestamp': 1601382711.433544}\n",
-      "Step Time :  0.00118255615234375\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 4, 3: 2, 8: 1}, 'inference_time': 4.076957702636719e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::1a88f3d68232723258cf91c3f90341a2', 'timestamp': 1601382711.434766}\n",
-      "Step Time :  0.0012896060943603516\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 4, 3: 2, 8: 2}, 'inference_time': 3.9577484130859375e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::9996e44a9b8f5e18f189aa8a20f0c826', 'timestamp': 1601382711.436093}\n",
-      "Step Time :  0.0028619766235351562\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 4, 3: 2, 8: 4}, 'inference_time': 9.632110595703125e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::1326f67ea516bea91837053b900c947b', 'timestamp': 1601382711.439057}\n",
-      "Step Time :  0.002596616744995117\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 4, 3: 2, 8: 4}, 'inference_time': 7.939338684082031e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::982362496c3b2ab2acebdf6d902cc350', 'timestamp': 1601382711.4417272}\n",
-      "Step Time :  0.0021734237670898438\n",
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-      "Step Time :  0.0010089874267578125\n",
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-      "Step Time :  0.00109100341796875\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 2, 6: 2, 9: 3}, 'inference_time': 5.3882598876953125e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::1e80fc419a1509d57c10236d99ba2d36', 'timestamp': 1601382711.7620974}\n",
-      "Step Time :  0.0009961128234863281\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {6: 2, 9: 2}, 'inference_time': 4.029273986816406e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::fc291b4273bd59e58aed523b3b973331', 'timestamp': 1601382711.763125}\n",
-      "Step Time :  0.0007307529449462891\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {6: 2, 9: 2}, 'inference_time': 3.6716461181640625e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::56a53182f6a692191f054987f22ccbf7', 'timestamp': 1601382711.7638884}\n",
-      "Step Time :  0.0007436275482177734\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {6: 2, 9: 2}, 'inference_time': 3.6716461181640625e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::42c1899fc8f66af0a518899e5f4b8095', 'timestamp': 1601382711.7646654}\n",
-      "Step Time :  0.0007314682006835938\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {9: 2}, 'inference_time': 3.910064697265625e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::d4ffadcd395448a903402e38bc0a84e7', 'timestamp': 1601382711.765435}\n",
-      "Step Time :  0.0006229877471923828\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {9: 2}, 'inference_time': 3.600120544433594e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::ea261726296cb7909bdebb41e425c339', 'timestamp': 1601382711.7660916}\n",
-      "Step Time :  0.0006310939788818359\n",
+      "...........................................................................................................................................................................\n",
       "Current Episode :  1\n",
       "Episode Done\n",
       "Reward :  10.0\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_CREATE', 'payload': {}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::978da518f555337ce6430a1a07b61df8', 'timestamp': 1601382711.7667947}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Response :  b'\\x80\\x03}q\\x00(X\\x04\\x00\\x00\\x00typeq\\x01X\\x1f\\x00\\x00\\x00FLATLAND_RL.ENV_CREATE_RESPONSEq\\x02X\\x07\\x00\\x00\\x00payloadq\\x03}q\\x04(X\\x0b\\x00\\x00\\x00observationq\\x05\\x88X\\r\\x00\\x00\\x00env_file_pathq\\x06X\\x12\\x00\\x00\\x00Test_0/Level_1.pklq\\x07X\\x04\\x00\\x00\\x00infoq\\x08}q\\t(X\\x0f\\x00\\x00\\x00action_requiredq\\n}q\\x0b(K\\x00\\x88K\\x01\\x88K\\x02\\x88K\\x03\\x88K\\x04\\x88K\\x05\\x88K\\x06\\x88K\\x07\\x88K\\x08\\x88K\\t\\x88uX\\x0b\\x00\\x00\\x00malfunctionq\\x0c}q\\r(K\\x00K\\x00K\\x01K\\x00K\\x02K\\x00K\\x03K\\x00K\\x04K\\x00K\\x05K\\x00K\\x06K\\x00K\\x07K\\x00K\\x08K\\x00K\\tK\\x00uX\\x05\\x00\\x00\\x00speedq\\x0e}q\\x0f(K\\x00G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x01G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x02G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x03G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x04G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x05G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x06G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x07G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\x08G?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00K\\tG?\\xf0\\x00\\x00\\x00\\x00\\x00\\x00uX\\x06\\x00\\x00\\x00statusq\\x10}q\\x11(K\\x00cflatland.envs.agent_utils\\nRailAgentStatus\\nq\\x12K\\x00\\x85q\\x13Rq\\x14K\\x01h\\x14K\\x02h\\x14K\\x03h\\x14K\\x04h\\x14K\\x05h\\x14K\\x06h\\x14K\\x07h\\x14K\\x08h\\x14K\\th\\x14uuX\\x0b\\x00\\x00\\x00random_seedq\\x15M\\xe9\\x03uu.'\n",
-      "Received Env :  Test_0/Level_1.pkl\n",
-      "Current env path :  ../env_data/tests/service_test/Test_0/Level_1.pkl\n",
       "DEPRECATED - use FileMalfunctionGen instead of malfunction_from_file\n",
       "DEPRECATED - RailEnv arg: malfunction_and_process_data - use malfunction_generator\n",
       "Episode : 1\n",
       "False\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 2}, 'inference_time': 7.557868957519531e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::fcbc1a592db4b9ab83ee4b82489be98d', 'timestamp': 1601382711.8173141}\n",
-      "Step Time :  0.002300262451171875\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 2}, 'inference_time': 9.107589721679688e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::01660f051b3f375a8a00683810f55df6', 'timestamp': 1601382711.8197002}\n",
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-      "Step Time :  0.0014221668243408203\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 3, 3: 1, 8: 3}, 'inference_time': 7.748603820800781e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::84fe3e3f27788eafc3e651a0b2dc6381', 'timestamp': 1601382711.826843}\n",
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-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 1}, 'inference_time': 5.340576171875e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::b31263b6c34f9fae6a601b49dcb269cf', 'timestamp': 1601382711.8307066}\n",
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-      "Step Time :  0.001547098159790039\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 2, 3: 2, 8: 3}, 'inference_time': 4.1484832763671875e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::7a5baaa32fae1f442e5d5097e8e7bd61', 'timestamp': 1601382711.8365853}\n",
-      "Step Time :  0.003201723098754883\n",
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-      "Step Time :  0.00093841552734375\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {9: 2}, 'inference_time': 4.029273986816406e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::d4ffadcd395448a903402e38bc0a84e7', 'timestamp': 1601382712.1766286}\n",
-      "Step Time :  0.0007486343383789062\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {9: 2}, 'inference_time': 3.695487976074219e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::ea261726296cb7909bdebb41e425c339', 'timestamp': 1601382712.1774101}\n",
-      "Step Time :  0.0007071495056152344\n",
+      "...........................................................................................................................................................................\n",
       "Current Episode :  2\n",
       "Episode Done\n",
       "Reward :  10.0\n",
-      "Request :  {'type': 'FLATLAND_RL.ENV_CREATE', 'payload': {}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::978da518f555337ce6430a1a07b61df8', 'timestamp': 1601382712.1781914}\n",
-      "Response :  b'\\x82\\xa4type\\xb1FLATLAND_RL.ERROR\\xa7payload\\x82\\xa4type\\xb1FLATLAND_RL.ERROR\\xa7payload\\xb7list index out of range'\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "unregistered extension code 164",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-26-c2b4e928c1b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         obs, info = oFRC.env_create(\n\u001b[0;32m----> 6\u001b[0;31m             \u001b[0mobs_builder_object\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moObsB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         )\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home3/jeremy/projects/aicrowd/rl-trains/flatland5/flatland/evaluators/client.py\u001b[0m in \u001b[0;36menv_create\u001b[0;34m(self, obs_builder_object)\u001b[0m\n\u001b[1;32m    234\u001b[0m         \u001b[0m_request\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmessages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFLATLAND_RL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mENV_CREATE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m         \u001b[0m_request\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'payload'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m         \u001b[0m_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_remote_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_request\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    237\u001b[0m         \u001b[0mobservation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_response\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'payload'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'observation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m         \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_response\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'payload'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'info'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home3/jeremy/projects/aicrowd/rl-trains/flatland5/flatland/evaluators/client.py\u001b[0m in \u001b[0;36m_remote_request\u001b[0;34m(self, _request, blocking)\u001b[0m\n\u001b[1;32m    190\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Response : \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_response\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_pickle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m                 \u001b[0m_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_response\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    193\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m                 _response = msgpack.unpackb(\n",
-      "\u001b[0;31mValueError\u001b[0m: unregistered extension code 164"
+      "null observation!\n",
+      "Evaluation Complete...\n",
+      "====================================================================================================\n",
+      "====================================================================================================\n",
+      "## Client Performance Stats\n",
+      "====================================================================================================\n",
+      "\t - env_creation_wait_time\t => min: 0.0011365413665771484 || mean: 0.007825930913289389 || max: 0.014734029769897461\n",
+      "\t - internal_env_reset_time\t => min: 0.0015034675598144531 || mean: 0.0029495954513549805 || max: 0.004395723342895508\n",
+      "\t - inference_time(approx)\t => min: 2.288818359375e-05 || mean: 6.703675141808588e-05 || max: 0.0007417201995849609\n",
+      "\t - internal_env_step_time\t => min: 0.0003027915954589844 || mean: 0.0009439960557814924 || max: 0.0038805007934570312\n",
+      "====================================================================================================\n",
+      "{'mean_reward': -944.0, 'mean_normalized_reward': 0.80735, 'mean_percentage_complete': 1.0}\n"
      ]
     }
    ],
@@ -1192,15 +400,20 @@
     "        print(oFRC.env.dones['__all__'])\n",
     "\n",
     "        while True:\n",
-    "            action = my_controller(obs, oFRC.env)\n",
+    "            if episode < 3:\n",
+    "                action = expert_controller(obs, oFRC.env)\n",
+    "            else:\n",
+    "                action = random_controller(obs, oFRC.env)\n",
+    "                \n",
     "            time_start = time.time()\n",
     "\n",
     "            try:\n",
     "                observation, all_rewards, done, info = oFRC.env_step(action)\n",
     "                time_diff = time.time() - time_start\n",
-    "                print(\"Step Time : \", time_diff)\n",
+    "                #print(\"Step Time : \", time_diff)\n",
+    "                print(\".\", end=\"\")\n",
     "                if done['__all__']:\n",
-    "                    print(\"Current Episode : \", episode)\n",
+    "                    print(\"\\nCurrent Episode : \", episode)\n",
     "                    print(\"Episode Done\")\n",
     "                    print(\"Reward : \", sum(list(all_rewards.values())))\n",
     "                    break\n",
@@ -1212,443 +425,20 @@
     "    print(oFRC.submit())"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "oFRC.env"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Render episode stuff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "env, env_dict = RailEnvPersister.load_new(sResource, load_from_package=sPack) # env_file)\n",
-    "\n",
-    "# the seed has to match that used to record the episode, in order for the malfunctions to match.\n",
-    "env.reset(random_seed=1001)\n",
-    "oRT = RenderTool(env, show_debug=True)\n",
-    "aImg = oRT.render_env(show_rowcols=True, return_image=True, show_inactive_agents=True)\n",
-    "print(env._max_episode_steps)\n",
-    "PIL.Image.fromarray(aImg)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loAgs = env_dict[\"agents\"]\n",
-    "lCols =  \"initial_direction,direction,initial_position,position\".split(\",\")\n",
-    "pd.DataFrame([ [getattr(oAg, sCol) for sCol in lCols] \n",
-    "              for oAg in loAgs], columns=lCols)"
+    "### Kill the evaluator process we started earlier"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame([ [getattr(oAg, sCol) for sCol in lCols] \n",
-    "              for oAg in env.agents], columns=lCols)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame([ vars(oAg) for oAg in env.agents])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "kkejL06T8xyU"
-   },
-   "outputs": [],
-   "source": [
-    "# from persistence.py\n",
-    "def get_agent_state(env):\n",
-    "    list_agents_state = []\n",
-    "    for iAg, oAg in enumerate(env.agents):\n",
-    "        # the int cast is to avoid numpy types which may cause problems with msgpack\n",
-    "        # in env v2, agents may have position None, before starting\n",
-    "        if oAg.position is None:\n",
-    "            pos = (0, 0)\n",
-    "        else:\n",
-    "            pos = (int(oAg.position[0]), int(oAg.position[1]))\n",
-    "        # print(\"pos:\", pos, type(pos[0]))\n",
-    "        list_agents_state.append(\n",
-    "            [*pos, int(oAg.direction), oAg.malfunction_data[\"malfunction\"]])\n",
-    "      \n",
-    "    return list_agents_state"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "uNmOgQjO-Rw_"
-   },
-   "outputs": [],
-   "source": [
-    "expert_actions = env_dict['actions']\n",
-    "episode_states = env_dict['episode']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame([ vars(oAg) for oAg in env.agents])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 34
-    },
-    "colab_type": "code",
-    "id": "IXQxfUXF2U33",
-    "outputId": "2a94ffec-c6b7-4cc3-d779-8c430d66918d",
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "env_renderer = RenderTool(env, gl=\"PGL\", show_debug=True)\n",
-    "\n",
-    "n_agents = env.get_num_agents()\n",
-    "x_dim, y_dim = env.width, env.height\n",
-    "max_steps = env_dict['max_episode_steps']\n",
-    "\n",
-    "action_dict = {}\n",
-    "frames = []\n",
-    "\n",
-    "# log everything in original state\n",
-    "statuses = []\n",
-    "for a in range(n_agents):\n",
-    "    statuses.append(env.agents[a].status)\n",
-    "pilImg = render_env(env_renderer)\n",
-    "frames.append({\n",
-    "    'image': pilImg,\n",
-    "    'statuses': statuses\n",
-    "    })\n",
-    "\n",
-    "step = 0\n",
-    "all_done = False\n",
-    "failed_action_check = False\n",
-    "print(\"Processing episode steps:\")\n",
-    "while not all_done and step < len(expert_actions):\n",
-    "    print(step, end=\", \")\n",
-    "    \"\"\"\n",
-    "    for a in range(n_agents):\n",
-    "      if info['action_required'][a]:\n",
-    "          if step < len(expert_actions):\n",
-    "            if a in expert_actions[step]:\n",
-    "              action = expert_actions[step][a]\n",
-    "            else:\n",
-    "              print(\"Step {}: agent {} needs action but not provided! only got {}\".format(step, a, expert_actions[step]))\n",
-    "          else:\n",
-    "              action = 0\n",
-    "\n",
-    "      action_dict.update({a: action})\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    if step < len(expert_actions):\n",
-    "        dAct = expert_actions[step]\n",
-    "    else:\n",
-    "        dAct = {}\n",
-    "    \n",
-    "    next_obs, all_rewards, done, info = env.step(dAct)\n",
-    "  \n",
-    "    if True:\n",
-    "        # Check that agent states match recorded states\n",
-    "        if get_agent_state(env) == episode_states[step]:\n",
-    "            pass\n",
-    "        else:\n",
-    "            print(\"MISMATCH\")\n",
-    "            failed_action_check = True\n",
-    "            #print(\"env:\", get_agent_state(env))\n",
-    "            #print(\"epi:\", episode_states[step])\n",
-    "            llAgSt = get_agent_state(env)\n",
-    "            llEpSt = episode_states[step]\n",
-    "            for iAg, (lAgSt, lEpSt) in enumerate(zip(llAgSt, llEpSt)):\n",
-    "                if lAgSt != lEpSt:\n",
-    "                    print(\"Ag:\", iAg, \"Env: \", lAgSt, \"Epi:\", lEpSt, end = \"; \")\n",
-    "            print(\"------\")\n",
-    "\n",
-    "  \n",
-    "    # Force agent states from the recorded states\n",
-    "    if False:\n",
-    "        for idx, agent in enumerate(env.agents):\n",
-    "            #print(episode_states[step][idx])\n",
-    "            rcPos = episode_states[step][idx][0:2]\n",
-    "            #print(idx, rcPos)\n",
-    "            if rcPos == [0,0]:\n",
-    "                agent.position = None\n",
-    "            else:\n",
-    "                agent.position = (*rcPos,) # episode_states[step][idx][0], episode_states[step][idx][1]#, episode_states[step][idx][2]\n",
-    "\n",
-    "            agent.malfunction_data[\"malfunction\"] = episode_states[step][idx][3]\n",
-    "            agent.direction = int(episode_states[step][idx][2])\n",
-    "\n",
-    "            agent.old_direction = int(episode_states[step-1][idx][2])\n",
-    "            agent.old_position = episode_states[step-1][idx][:2]\n",
-    "\n",
-    "    statuses = []\n",
-    "    for a in range(n_agents):\n",
-    "        statuses.append(env.agents[a].status)\n",
-    "\n",
-    "    #clear_output(wait=True)\n",
-    "    pilImg = render_env(env_renderer)\n",
-    "    frames.append({\n",
-    "        'image': pilImg,\n",
-    "        'statuses': statuses\n",
-    "      })\n",
-    "    #print(\"Replaying {}/{}\".format(step, max_steps))\n",
-    "\n",
-    "    if done['__all__']:\n",
-    "        all_done = True\n",
-    "        max_steps = step + 1\n",
-    "        print(\"done\")\n",
-    "\n",
-    "    step += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "assert failed_action_check == False, \"Realised states did not match stored states.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "env.agents[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "[ oAg.malfunction_data for oAg in env_dict[\"agents\"] ][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "env_dict[\"malfunction\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "env._max_episode_steps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sfImg = sResource.replace(\"pkl\", \"gif\")\n",
-    "imageio.mimsave(sfImg, [d[\"image\"] for d in frames], subrectangles=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sfImg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "display.Image(sfImg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 577,
-     "referenced_widgets": [
-      "11ecd9887b824225b037d043745c7f19",
-      "d46bfa4e89d44814acb57a247aa60fec",
-      "711e232d2cb54266a52552d90ce3f05e",
-      "176dbef2d38244bcb256258a692d53a7",
-      "81330f0e89e149e697e26eb0ed80d023",
-      "b60ecb9134eb449088fd35d3ee9dd20e",
-      "0a2d6b655cc6486bba4cb2ed086b2a19",
-      "6870e3259fa24df5b6117eadcba5a84f",
-      "3a991f2384ec4a0984a850c380fdf8c5",
-      "707d4424f540458dbb43cd52177eddf3",
-      "c17016ecd7e24c20a2240f5cb8e1bca1",
-      "c7d9b8f474144b8b8311ab87338efc64"
-     ]
-    },
-    "colab_type": "code",
-    "id": "uFzeelj2FW9n",
-    "outputId": "3b65f192-301d-4c98-cf0c-c987433003ef"
-   },
-   "outputs": [],
-   "source": [
-    "from ipywidgets import interact, interactive, fixed, interact_manual, Play\n",
-    "import ipywidgets as widgets\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import numpy as np\n",
-    "from IPython.display import HTML\n",
-    "\n",
-    "display.display(HTML('<link rel=\"stylesheet\" href=\"//stackpath.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css\"/>'))\n",
-    "\n",
-    "def plot_func(frame_idx):\n",
-    "    frame = frames[int(frame_idx)]\n",
-    "    display.display(frame['image'])\n",
-    "    #print(frame['statuses'])\n",
-    "\n",
-    "if True:\n",
-    "    slider = widgets.FloatSlider(value=0, min=0, max=max_steps, step=1)\n",
-    "    interact(plot_func, frame_idx = slider)\n",
-    "\n",
-    "    play = Play(\n",
-    "        max=max_steps,\n",
-    "        value=0,\n",
-    "        step=1,\n",
-    "        interval=250\n",
-    "    )\n",
-    "\n",
-    "    widgets.link((play, 'value'), (slider, 'value'))\n",
-    "    widgets.VBox([play])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "j9_J2f6K64Jb"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g3Ep = np.array(episode_states)\n",
-    "np.sum(g3Ep[:,:,3] > 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(np.sum(g3Ep[:,:,3]>0, axis=1))\n",
-    "plt.title(sResource + \"\\nmalfunctioning agents by time step\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g3Ep.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g3Ep2 = np.array(env.cur_episode)\n",
-    "g3Ep2.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.step(range(len(g3Ep2)), np.sum(g3Ep2[:,:,4]==1, axis=1), label=\"Active\")\n",
-    "plt.step(range(len(g3Ep2)), np.sum(g3Ep2[:,:,4]==0, axis=1), label=\"Ready to depart\")\n",
-    "plt.title(\"env: \"+ sResource +\"\\nActive Agents by timestep\")\n",
-    "plt.legend()"
+    "!ps -ef | grep -i python | grep -i flatland.evaluators.service | awk '{print $2}' | xargs kill"
    ]
   }
  ],
-- 
GitLab