akanametov
commited on
Commit
•
4530712
1
Parent(s):
b3c5f95
Trained SoccerTwos agent upload
Browse files- README.md +31 -0
- SoccerTwos.onnx +3 -0
- SoccerTwos/SoccerTwos-1026228.onnx +3 -0
- SoccerTwos/SoccerTwos-1026228.pt +3 -0
- SoccerTwos/SoccerTwos-499114.onnx +3 -0
- SoccerTwos/SoccerTwos-499114.pt +3 -0
- SoccerTwos/SoccerTwos-999766.onnx +3 -0
- SoccerTwos/SoccerTwos-999766.pt +3 -0
- SoccerTwos/checkpoint.pt +3 -0
- SoccerTwos/events.out.tfevents.1673807871.697baef2d0c0.6167.0 +3 -0
- config.json +1 -0
- configuration.yaml +81 -0
- run_logs/Player-0.log +59 -0
- run_logs/timers.json +431 -0
- run_logs/training_status.json +48 -0
README.md
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---
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tags:
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- unity-ml-agents
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- ml-agents
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- deep-reinforcement-learning
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- reinforcement-learning
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- ML-Agents-SoccerTwos
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library_name: ml-agents
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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** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
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## Usage (with ML-Agents)
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The Documentation: https://github.com/huggingface/ml-agents#get-started
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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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### Resume the training
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```
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mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
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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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1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
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2. Step 1: Write your model_id: akanametov/MLAgents-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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SoccerTwos.onnx
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version https://git-lfs.github.com/spec/v1
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size 1781689
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SoccerTwos/SoccerTwos-1026228.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:631237d1d2ef4a263077f627c8aa1e4c014440d7fff6b2043cf566a0a97b120c
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size 1781689
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SoccerTwos/SoccerTwos-1026228.pt
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version https://git-lfs.github.com/spec/v1
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size 28590523
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SoccerTwos/SoccerTwos-499114.onnx
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version https://git-lfs.github.com/spec/v1
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size 1781689
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SoccerTwos/SoccerTwos-499114.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:01301570baea05e0c9b9b890f44f59af5238fdf1848529481c109190cc54119a
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size 28590523
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SoccerTwos/SoccerTwos-999766.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:d15b056d110fcbda462a2876bbe095d9b25036082e138e1083d3cd4ee1190db2
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size 1781689
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SoccerTwos/SoccerTwos-999766.pt
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version https://git-lfs.github.com/spec/v1
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size 28590523
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SoccerTwos/checkpoint.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:d79fa8f9fa863c75c209f6986229f3a249d7416be71d67f0f5da6e513f7eb903
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size 28590523
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SoccerTwos/events.out.tfevents.1673807871.697baef2d0c0.6167.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:b2b22976af17e534828fa4100c11331e0eacbef85a84e20639b64d17ac9fa2f7
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size 545780
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config.json
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{"default_settings": null, "behaviors": {"SoccerTwos": {"trainer_type": "poca", "hyperparameters": {"batch_size": 2048, "buffer_size": 20480, "learning_rate": 0.0003, "beta": 0.005, "epsilon": 0.2, "lambd": 0.95, "num_epoch": 3, "learning_rate_schedule": "constant", "beta_schedule": "constant", "epsilon_schedule": "constant"}, "network_settings": {"normalize": false, "hidden_units": 512, "num_layers": 2, "vis_encode_type": "simple", "memory": null, "goal_conditioning_type": "hyper", "deterministic": false}, "reward_signals": {"extrinsic": {"gamma": 0.99, "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, "checkpoint_interval": 500000, "max_steps": 50000000, "time_horizon": 1000, "summary_freq": 10000, "threaded": false, "self_play": {"save_steps": 50000, "team_change": 200000, "swap_steps": 2000, "window": 10, "play_against_latest_model_ratio": 0.5, "initial_elo": 1200.0}, "behavioral_cloning": null}}, "env_settings": {"env_path": "./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos", "env_args": null, "base_port": 5005, "num_envs": 1, "num_areas": 1, "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}, "environment_parameters": null, "checkpoint_settings": {"run_id": "SoccerTwos", "initialize_from": null, "load_model": false, "resume": false, "force": false, "train_model": false, "inference": false, "results_dir": "results"}, "torch_settings": {"device": null}, "debug": false}
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configuration.yaml
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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: 0.0003
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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: 3
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learning_rate_schedule: constant
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beta_schedule: constant
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epsilon_schedule: constant
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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: 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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reward_signals:
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extrinsic:
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gamma: 0.99
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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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checkpoint_interval: 500000
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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: 2000
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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/linux/linux-x86_64/soccer-twos/soccer-twos
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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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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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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: false
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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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run_logs/Player-0.log
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Mono path[0] = '/content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Managed'
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Mono config path = '/content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/MonoBleedingEdge/etc'
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Preloaded 'lib_burst_generated.so'
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Preloaded 'libgrpc_csharp_ext.x64.so'
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Initialize engine version: 2019.4.30f1 (e8c891080a1f)
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[Subsystems] Discovering subsystems at path /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/UnitySubsystems
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Forcing GfxDevice: Null
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GfxDevice: creating device client; threaded=0
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NullGfxDevice:
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Version: NULL 1.0 [1.0]
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Renderer: Null Device
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Vendor: Unity Technologies
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ALSA lib confmisc.c:767:(parse_card) cannot find card '0'
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ALSA lib conf.c:4528:(_snd_config_evaluate) function snd_func_card_driver returned error: No such file or directory
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ALSA lib confmisc.c:392:(snd_func_concat) error evaluating strings
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ALSA lib conf.c:4528:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
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ALSA lib confmisc.c:1246:(snd_func_refer) error evaluating name
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ALSA lib conf.c:4528:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
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ALSA lib conf.c:5007:(snd_config_expand) Evaluate error: No such file or directory
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ALSA lib pcm.c:2495:(snd_pcm_open_noupdate) Unknown PCM default
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FMOD failed to initialize the output device.: "Error initializing output device. " (60)
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(Filename: Line: 1652)
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Forced to initialize FMOD to to the device driver's system output rate 48000, this may impact performance and/or give inconsistent experiences compared to selected sample rate 48000
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(Filename: Line: 1588)
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ALSA lib confmisc.c:767:(parse_card) cannot find card '0'
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ALSA lib conf.c:4528:(_snd_config_evaluate) function snd_func_card_driver returned error: No such file or directory
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ALSA lib confmisc.c:392:(snd_func_concat) error evaluating strings
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ALSA lib conf.c:4528:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
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ALSA lib confmisc.c:1246:(snd_func_refer) error evaluating name
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ALSA lib conf.c:4528:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
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ALSA lib conf.c:5007:(snd_config_expand) Evaluate error: No such file or directory
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ALSA lib pcm.c:2495:(snd_pcm_open_noupdate) Unknown PCM default
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FMOD failed to initialize the output device.: "Error initializing output device. " (60)
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(Filename: Line: 1652)
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FMOD initialized on nosound output
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Begin MonoManager ReloadAssembly
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- Completed reload, in 0.067 seconds
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UnloadTime: 0.881175 ms
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libcoreclr.so
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libcoreclr.so
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libcoreclr.so
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libSystem.dylib
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libSystem.dylib.so
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libSystem.dylib
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libcoreclr.so
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libcoreclr.so
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libcoreclr.so
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libSystem.dylib
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libSystem.dylib.so
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Fallback handler could not load library /content/ml-agents/./training-envs-executables/linux/linux-x86_64/soccer-twos/soccer-twos_Data/Mono/libSystem.dylib
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EnvConfigurationChannel is set up.
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(Filename: ./Runtime/Export/Debug/Debug.bindings.h Line: 39)
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requesting resize 84 x 84
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Setting up 1 worker threads for Enlighten.
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Thread -> id: 7fa63cbfa700 -> priority: 1
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run_logs/timers.json
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run_logs/training_status.json
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@@ -0,0 +1,48 @@
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