diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ad1d47f933b9147f53f257178ae19929f3d2c948 --- /dev/null +++ b/README.md @@ -0,0 +1,29 @@ +--- +library_name: ml-agents +tags: +- SoccerTwos +- deep-reinforcement-learning +- reinforcement-learning +- ML-Agents-SoccerTwos +--- + + # **poca** Agent playing **SoccerTwos** + This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). + + ## Usage (with ML-Agents) + The Documentation: https://github.com/huggingface/ml-agents#get-started + We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: + + + ### Resume the training + ``` + mlagents-learn --run-id= --resume + ``` + ### Watch your Agent play + You can watch your agent **playing directly in your browser:**. + + 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos + 2. Step 1: Find your model_id: Hans14/poca-SoccerTwos + 3. Step 2: Select your *.nn /*.onnx file + 4. 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+{"default_settings": null, "behaviors": {"SoccerTwos": {"trainer_type": "poca", "hyperparameters": {"batch_size": 2048, "buffer_size": 20480, "learning_rate": 0.0005, "beta": 0.005, "epsilon": 0.2, "lambd": 0.95, "num_epoch": 3, "learning_rate_schedule": "constant", "beta_schedule": "constant", "epsilon_schedule": "constant"}, "checkpoint_interval": 100000, "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, "even_checkpoints": false, "max_steps": 5000000, "time_horizon": 1000, "summary_freq": 25000, "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/SoccerTwos/SoccerTwos.x86_64", "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": "poca-v1", "initialize_from": null, "load_model": false, "resume": true, "force": false, "train_model": false, "inference": false, "results_dir": "/content/gdrive/MyDrive/Colab_Model/SoccerTwos"}, "torch_settings": {"device": null}, "debug": false} \ No newline at end of file diff --git a/configuration.yaml b/configuration.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4bb0eddc8aa746769f321e77f95133c5cb3c4262 --- /dev/null +++ b/configuration.yaml @@ -0,0 +1,82 @@ +default_settings: null +behaviors: + SoccerTwos: + trainer_type: poca + hyperparameters: + batch_size: 2048 + buffer_size: 20480 + learning_rate: 0.0005 + beta: 0.005 + epsilon: 0.2 + lambd: 0.95 + num_epoch: 3 + learning_rate_schedule: constant + beta_schedule: constant + epsilon_schedule: constant + checkpoint_interval: 100000 + 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 + even_checkpoints: false + max_steps: 5000000 + time_horizon: 1000 + summary_freq: 25000 + 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/SoccerTwos/SoccerTwos.x86_64 + 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: poca-v1 + initialize_from: null + load_model: false + resume: true + force: false + train_model: false + inference: false + results_dir: /content/gdrive/MyDrive/Colab_Model/SoccerTwos +torch_settings: + device: null +debug: false diff --git a/run_config.yaml b/run_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..62274ca93eb865f52112c6afb7301c1ae9afdedb --- /dev/null +++ b/run_config.yaml @@ -0,0 +1,33 @@ +behaviors: + SoccerTwos: + trainer_type: poca + hyperparameters: + batch_size: 2048 + buffer_size: 20480 + learning_rate: 0.0005 + beta: 0.005 + epsilon: 0.2 + lambd: 0.95 + num_epoch: 3 + learning_rate_schedule: constant + network_settings: + normalize: false + hidden_units: 512 + num_layers: 2 + vis_encode_type: simple + reward_signals: + extrinsic: + gamma: 0.99 + strength: 1.0 + keep_checkpoints: 5 + checkpoint_interval: 100000 + max_steps: 5000000 + time_horizon: 1000 + summary_freq: 25000 + self_play: + save_steps: 50000 + team_change: 200000 + swap_steps: 2000 + window: 10 + play_against_latest_model_ratio: 0.5 + initial_elo: 1200.0 diff --git a/run_logs/Player-0.log b/run_logs/Player-0.log new file mode 100644 index 0000000000000000000000000000000000000000..714731698f8ae4613387254b51a7ef3166521dc7 --- /dev/null +++ b/run_logs/Player-0.log @@ -0,0 +1,277 @@ +Mono path[0] = '/content/ml-agents/training-envs-executables/linux/SoccerTwos/SoccerTwos_Data/Managed' +Mono config path = '/content/ml-agents/training-envs-executables/linux/SoccerTwos/SoccerTwos_Data/MonoBleedingEdge/etc' +Preloaded 'lib_burst_generated.so' +Preloaded 'libgrpc_csharp_ext.x64.so' +PlayerPrefs - Creating folder: /root/.config/unity3d/Unity Technologies +PlayerPrefs - Creating folder: /root/.config/unity3d/Unity Technologies/SoccerTwos +Unable to load player prefs +Initialize engine version: 2021.3.14f1 (eee1884e7226) +[Subsystems] Discovering subsystems at path /content/ml-agents/training-envs-executables/linux/SoccerTwos/SoccerTwos_Data/UnitySubsystems +Forcing GfxDevice: Null +GfxDevice: creating device client; threaded=0; jobified=0 +NullGfxDevice: + Version: NULL 1.0 [1.0] + Renderer: Null Device + Vendor: Unity Technologies +ALSA lib confmisc.c:767:(parse_card) cannot find card '0' +ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_card_driver returned error: No such file or directory +ALSA lib confmisc.c:392:(snd_func_concat) error evaluating strings +ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory +ALSA lib confmisc.c:1246:(snd_func_refer) error evaluating name +ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory +ALSA lib conf.c:5220:(snd_config_expand) Evaluate error: No such file or directory +ALSA lib pcm.c:2642:(snd_pcm_open_noupdate) Unknown PCM default +FMOD failed to initialize the output device.: "Error initializing output device. " (60) +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 +ALSA lib confmisc.c:767:(parse_card) cannot find card '0' +ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_card_driver returned error: No such file or directory +ALSA lib confmisc.c:392:(snd_func_concat) error evaluating strings +ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory +ALSA lib confmisc.c:1246:(snd_func_refer) error evaluating name +ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory +ALSA lib conf.c:5220:(snd_config_expand) Evaluate error: No such file or directory +ALSA lib pcm.c:2642:(snd_pcm_open_noupdate) Unknown PCM default +FMOD failed to initialize the output device.: "Error initializing output device. 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