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", - 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"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", - 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"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", - "Step Time : 0.0027971267700195312\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {1: 1, 3: 2, 8: 4}, 'inference_time': 0.00011920928955078125}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::8f8a0507d4ce4aaff58d3754c2470535', 'timestamp': 1601382711.8226404}\n", - 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"Step Time : 0.0009696483612060547\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 2, 6: 1, 9: 2}, 'inference_time': 5.435943603515625e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::a7dcf510d64897e48436aef157a7577b', 'timestamp': 1601382712.149192}\n", - "Step Time : 0.0010528564453125\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 3, 6: 2, 9: 1}, 'inference_time': 7.176399230957031e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::e57305c5bbb277527075d44fbcc34eb9', 'timestamp': 1601382712.150317}\n", - "Step Time : 0.002578258514404297\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 2, 6: 3, 9: 2}, 'inference_time': 0.00022363662719726562}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::daf43f031b1523398b37cf1af0a5ad15', 'timestamp': 1601382712.1531286}\n", - "Step Time : 0.0029299259185791016\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 2, 6: 2, 9: 3}, 'inference_time': 0.0001938343048095703}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::d7d063eda82875334e16e2a4e9b56f88', 'timestamp': 1601382712.1562245}\n", - "Step Time : 0.0034918785095214844\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 1, 6: 2, 9: 2}, 'inference_time': 0.00017881393432617188}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::462849ec1b6d7e2718733acc779e4946', 'timestamp': 1601382712.1598952}\n", - "Step Time : 0.0015392303466796875\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {6: 1, 9: 2, 5: 3}, 'inference_time': 6.532669067382812e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::e787857c3553fc7487f7b1a24ec05c85', 'timestamp': 1601382712.161469}\n", - "Step Time : 0.0009663105010986328\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {9: 1, 5: 2, 6: 3}, 'inference_time': 3.910064697265625e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::b1d5c8f48dd1d6b9797e3aa4a9414368', 'timestamp': 1601382712.162464}\n", - "Step Time : 0.0008671283721923828\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 3, 6: 2, 9: 3}, 'inference_time': 3.7670135498046875e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::36764fbc1bad80022e07d6d070a4cf3b', 'timestamp': 1601382712.1633656}\n", - "Step Time : 0.0008323192596435547\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 4, 6: 3, 9: 4}, 'inference_time': 3.719329833984375e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::4033cdda7b15db1df7eed911770d5675', 'timestamp': 1601382712.1642327}\n", - "Step Time : 0.0008242130279541016\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 2, 6: 3, 9: 2}, 'inference_time': 3.600120544433594e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::a84da82a3a9bfa7990ed4532736fbbd6', 'timestamp': 1601382712.1650908}\n", - "Step Time : 0.0009586811065673828\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 2, 6: 3, 9: 4}, 'inference_time': 0.0001385211944580078}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::56ab4cf23e8c7d53e7959d71142778bf', 'timestamp': 1601382712.166219}\n", - "Step Time : 0.00138092041015625\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 2, 6: 3, 9: 3}, 'inference_time': 5.1021575927734375e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::3c972f53f98bba671cbf3fb93e597da5', 'timestamp': 1601382712.1676173}\n", - "Step Time : 0.0009887218475341797\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 2, 6: 2, 9: 3}, 'inference_time': 5.507469177246094e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::b734928ed9ed281a9672ab0ef4c5e939', 'timestamp': 1601382712.1686585}\n", - "Step Time : 0.00098419189453125\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {5: 2, 6: 2, 9: 3}, 'inference_time': 6.151199340820312e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::1e80fc419a1509d57c10236d99ba2d36', 'timestamp': 1601382712.1696985}\n", - "Step Time : 0.0015869140625\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {6: 2, 9: 2}, 'inference_time': 0.00023221969604492188}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::fc291b4273bd59e58aed523b3b973331', 'timestamp': 1601382712.1715674}\n", - "Step Time : 0.0019605159759521484\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {6: 2, 9: 2}, 'inference_time': 7.748603820800781e-05}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::56a53182f6a692191f054987f22ccbf7', 'timestamp': 1601382712.1735454}\n", - "Step Time : 0.0019025802612304688\n", - "Request : {'type': 'FLATLAND_RL.ENV_STEP', 'payload': {'action': {6: 2, 9: 2}, 'inference_time': 0.000225067138671875}, 'response_channel': 'flatland-rl::FLATLAND_RL_SERVICE_ID::response::42c1899fc8f66af0a518899e5f4b8095', 'timestamp': 1601382712.1756618}\n", - "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