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README.md ADDED
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+ ---
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+ library_name: ml-agents
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+ tags:
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+ - SoccerTwos
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - ML-Agents-SoccerTwos
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+ ---
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+
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+ # **poca** Agent playing **SoccerTwos**
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+ This is a trained model of a **poca** agent playing **SoccerTwos**
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+ using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
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+
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+ ## Usage (with ML-Agents)
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+ The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
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+
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+ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
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+ - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
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+ browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
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+ - A *longer tutorial* to understand how works ML-Agents:
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+ https://huggingface.co/learn/deep-rl-course/unit5/introduction
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+
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+ ### Resume the training
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+ ```bash
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+ mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
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+ ```
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+
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+ ### Watch your Agent play
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+ You can watch your agent **playing directly in your browser**
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+
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+ 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
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+ 2. Step 1: Find your model_id: jjmcarrascosa/poca-SoccerTwos
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+ 3. Step 2: Select your *.nn /*.onnx file
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+ 4. Click on Watch the agent play 👀
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+
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+ {"default_settings": null, "behaviors": {"SoccerTwos": {"trainer_type": "poca", "hyperparameters": {"batch_size": 2048, "buffer_size": 20480, "learning_rate": 0.0004, "beta": 0.005, "epsilon": 0.2, "lambd": 0.95, "num_epoch": 4, "learning_rate_schedule": "linear", "beta_schedule": "linear", "epsilon_schedule": "linear"}, "checkpoint_interval": 500000, "network_settings": {"normalize": false, "hidden_units": 512, "num_layers": 3, "vis_encode_type": "nature_cnn", "memory": null, "goal_conditioning_type": "hyper", "deterministic": false}, "reward_signals": {"extrinsic": {"gamma": 0.995, "strength": 1.0, "network_settings": {"normalize": false, "hidden_units": 128, "num_layers": 2, "vis_encode_type": "simple", "memory": null, "goal_conditioning_type": "hyper", "deterministic": false}}}, "init_path": null, "keep_checkpoints": 5, "even_checkpoints": false, "max_steps": 50000000, "time_horizon": 1000, "summary_freq": 10000, "threaded": false, "self_play": {"save_steps": 50000, "team_change": 200000, "swap_steps": 10000, "window": 10, "play_against_latest_model_ratio": 0.5, "initial_elo": 1200.0}, "behavioral_cloning": null}}, "env_settings": {"env_path": "./training-envs-executables/SoccerTwos.exe", "env_args": null, "base_port": 5005, "num_envs": 1, "num_areas": 1, "timeout_wait": 60, "seed": -1, "max_lifetime_restarts": 10, "restarts_rate_limit_n": 1, "restarts_rate_limit_period_s": 60}, "engine_settings": {"width": 84, "height": 84, "quality_level": 5, "time_scale": 20, "target_frame_rate": -1, "capture_frame_rate": 60, "no_graphics": true, "no_graphics_monitor": false}, "environment_parameters": null, "checkpoint_settings": {"run_id": "SoccerTwos", "initialize_from": null, "load_model": false, "resume": false, "force": true, "train_model": false, "inference": false, "results_dir": "results"}, "torch_settings": {"device": null}, "debug": false}
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+ default_settings: null
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+ behaviors:
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+ SoccerTwos:
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+ trainer_type: poca
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+ hyperparameters:
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+ batch_size: 2048
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+ buffer_size: 20480
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+ learning_rate: 0.0004
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+ beta: 0.005
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+ epsilon: 0.2
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+ lambd: 0.95
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+ num_epoch: 4
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+ learning_rate_schedule: linear
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+ beta_schedule: linear
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+ epsilon_schedule: linear
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+ checkpoint_interval: 500000
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+ network_settings:
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+ normalize: false
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+ hidden_units: 512
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+ num_layers: 3
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+ vis_encode_type: nature_cnn
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+ memory: null
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+ goal_conditioning_type: hyper
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+ deterministic: false
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+ reward_signals:
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+ extrinsic:
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+ gamma: 0.995
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+ strength: 1.0
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+ network_settings:
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+ normalize: false
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+ hidden_units: 128
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+ num_layers: 2
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+ vis_encode_type: simple
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+ memory: null
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+ goal_conditioning_type: hyper
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+ deterministic: false
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+ init_path: null
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+ keep_checkpoints: 5
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+ even_checkpoints: false
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+ max_steps: 50000000
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+ time_horizon: 1000
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+ summary_freq: 10000
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+ threaded: false
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+ self_play:
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+ save_steps: 50000
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+ team_change: 200000
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+ swap_steps: 10000
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+ window: 10
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+ play_against_latest_model_ratio: 0.5
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+ initial_elo: 1200.0
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+ behavioral_cloning: null
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+ env_settings:
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+ env_path: ./training-envs-executables/SoccerTwos.exe
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+ env_args: null
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+ base_port: 5005
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+ num_envs: 1
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+ num_areas: 1
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+ timeout_wait: 60
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+ seed: -1
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+ max_lifetime_restarts: 10
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+ restarts_rate_limit_n: 1
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+ restarts_rate_limit_period_s: 60
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+ engine_settings:
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+ width: 84
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+ height: 84
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+ quality_level: 5
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+ time_scale: 20
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+ target_frame_rate: -1
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+ capture_frame_rate: 60
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+ no_graphics: true
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+ no_graphics_monitor: false
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+ environment_parameters: null
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+ checkpoint_settings:
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+ run_id: SoccerTwos
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+ initialize_from: null
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+ load_model: false
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+ resume: false
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+ force: true
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+ train_model: false
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+ inference: false
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+ results_dir: results
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+ torch_settings:
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+ device: null
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+ debug: false
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+ Mono path[0] = 'D:/backup/PycharmProjects/ml-agents/training-envs-executables/SoccerTwos/SoccerTwos_Data/Managed'
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+ Mono config path = 'D:/backup/PycharmProjects/ml-agents/training-envs-executables/SoccerTwos/MonoBleedingEdge/etc'
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+ [Subsystems] Discovering subsystems at path D:/backup/PycharmProjects/ml-agents/training-envs-executables/SoccerTwos/SoccerTwos_Data/UnitySubsystems
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+ Vendor: Unity Technologies
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+ WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
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1271
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1272
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1275
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1300
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1302
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1304
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1305
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1306
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1307
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1308
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1309
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1310
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1311
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1312
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1313
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1314
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1315
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1316
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1317
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1318
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1319
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1320
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1321
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1322
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1323
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1324
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1325
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1326
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1327
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1328
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1329
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1330
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1331
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1332
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1333
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1334
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1335
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1336
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1337
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1338
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1339
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1340
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1341
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1342
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1343
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1344
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1345
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1346
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1347
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1348
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1349
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1350
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1351
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1352
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1353
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1354
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1355
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1356
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1357
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1358
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1359
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1360
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1361
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1362
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1363
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1364
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1365
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1366
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1367
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1368
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1369
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1370
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1371
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1372
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1373
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1374
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1375
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1376
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1377
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1378
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1379
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1380
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1381
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1382
+ Initial Block Size 32.0 KB
1383
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1384
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1385
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1386
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1387
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1388
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1389
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1390
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1391
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1392
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1393
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1394
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1395
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1396
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1397
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1398
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1399
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1400
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1401
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1402
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1403
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1404
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1405
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1406
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1407
+ Initial Block Size 256.0 KB
1408
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1409
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1410
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1411
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1412
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1413
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1414
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1415
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1416
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1417
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1418
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1419
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1420
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1421
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1422
+ Initial Block Size 256.0 KB
1423
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1424
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1425
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1426
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1427
+ Initial Block Size 256.0 KB
1428
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1429
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1430
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1431
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1432
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1433
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1434
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1435
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1436
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1437
+ Initial Block Size 64.0 KB
1438
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1439
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1440
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1441
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1442
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1443
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1444
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1445
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1446
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1447
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1448
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1449
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1450
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1451
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1452
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1453
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1454
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1455
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1456
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1457
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1458
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1459
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1460
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1461
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1462
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1463
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1464
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1465
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1466
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1467
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1468
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1469
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1470
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1471
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1472
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1473
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1474
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1475
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1476
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1477
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1478
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1479
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1480
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1481
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1482
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1483
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1484
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1485
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1486
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1487
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1488
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1489
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1490
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1491
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1492
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1493
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1494
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1495
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1496
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1497
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1498
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1499
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1500
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1501
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1503
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1506
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1507
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1508
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1514
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1518
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1522
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1524
+ Peak usage frame count: [16.0 MB-32.0 MB]: 287409 frames
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1531
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+ Large Block size 4.0 MB
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1556
+ Peak usage frame count: [32.0 KB-64.0 KB]: 287408 frames, [64.0 KB-128.0 KB]: 1 frames
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+ Peak Block count 1
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+ Peak Allocated memory 65.6 KB
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1562
+ Peak usage frame count: [128.0 KB-256.0 KB]: 287409 frames
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+ Peak Allocated memory 173.6 KB
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+ Large Block size 4.0 MB
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+ Peak Allocated bytes 0.9 MB
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1574
+ Peak usage frame count: [0.5 MB-1.0 MB]: 287409 frames
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+ Requested Block Size 4.0 MB
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+ Peak Block count 1
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+ Peak Allocated memory 0.6 MB
1578
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+ [ALLOC_CACHEOBJECTS_THREAD]
1580
+ Peak usage frame count: [0.5 MB-1.0 MB]: 287408 frames, [2.0 MB-4.0 MB]: 1 frames
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+ Requested Block Size 4.0 MB
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+ [ALLOC_TYPETREE] Dual Thread Allocator
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+ Large Block size 4.0 MB
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+ Peak Allocated bytes 0.9 MB
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+ [ALLOC_TYPETREE_MAIN]
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+ Peak usage frame count: [0-1.0 KB]: 287409 frames
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+ Requested Block Size 2.0 MB
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+ Peak Block count 1
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+ Peak Allocated memory 1.0 KB
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+ Peak Large allocation bytes 0 B
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+ [ALLOC_TYPETREE_THREAD]
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+ Peak usage frame count: [1.0 KB-2.0 KB]: 287408 frames, [2.0 KB-4.0 KB]: 1 frames
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+ Requested Block Size 2.0 MB
1600
+ Peak Block count 1
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+ Peak Allocated memory 2.2 KB
1602
+ Peak Large allocation bytes 0 B
run_logs/timers.json ADDED
@@ -0,0 +1,368 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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