diff --git a/.ipynb_checkpoints/configuration-checkpoint.yaml b/.ipynb_checkpoints/configuration-checkpoint.yaml new file mode 100644 index 0000000000000000000000000000000000000000..86d0655a3c8b18629bfff7ac87ed7f6c6175b274 --- /dev/null +++ b/.ipynb_checkpoints/configuration-checkpoint.yaml @@ -0,0 +1,82 @@ +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 + checkpoint_interval: 500000 + 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: 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: /app/ml-agents/training-envs-executables/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: SoccerTwos + initialize_from: null + load_model: false + resume: true + force: false + train_model: false + inference: false + results_dir: results +torch_settings: + device: null +debug: false diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b22fa7f88248c8b94c1ba3e5c91466501cb93c8e --- /dev/null +++ b/README.md @@ -0,0 +1,31 @@ + +--- + tags: + - unity-ml-agents + - ml-agents + - deep-reinforcement-learning + - reinforcement-learning + - ML-Agents-SoccerTwos + library_name: ml-agents +--- + + # **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: Write your model_id: WilliamADSP/poca-SoccerTwos-v3 + 3. Step 2: Select your *.nn /*.onnx file + 4. 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"memory": null, "goal_conditioning_type": "hyper", "deterministic": false}}}, "init_path": null, "keep_checkpoints": 5, "even_checkpoints": false, "max_steps": 30000000, "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": "/app/ml-agents/training-envs-executables/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": "SoccerTwos", "initialize_from": null, "load_model": false, "resume": true, "force": false, "train_model": false, "inference": false, "results_dir": "results"}, "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..afd92ea3fb1f51730eada09cafcddabad9c030eb --- /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: 0.0003 + beta: 0.005 + epsilon: 0.2 + lambd: 0.95 + num_epoch: 3 + learning_rate_schedule: constant + beta_schedule: constant + epsilon_schedule: constant + checkpoint_interval: 500000 + 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: 30000000 + 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: /app/ml-agents/training-envs-executables/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: SoccerTwos + initialize_from: null + load_model: false + resume: true + force: false + train_model: false + inference: false + results_dir: results +torch_settings: + device: null +debug: false diff --git a/run_logs/Player-0.log b/run_logs/Player-0.log new file mode 100644 index 0000000000000000000000000000000000000000..48766ea10f6a675e794cdc9d9ad4bc06ff777502 --- /dev/null +++ b/run_logs/Player-0.log @@ -0,0 +1,285 @@ +Mono path[0] = '/app/ml-agents/training-envs-executables/SoccerTwos/SoccerTwos_Data/Managed' +Mono config path = '/app/ml-agents/training-envs-executables/SoccerTwos/SoccerTwos_Data/MonoBleedingEdge/etc' +Preloaded 'lib_burst_generated.so' +Preloaded 'libgrpc_csharp_ext.x64.so' +Initialize engine version: 2021.3.14f1 (eee1884e7226) +[Subsystems] Discovering subsystems at path /app/ml-agents/training-envs-executables/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 +FMOD initialized on nosound output +Begin MonoManager ReloadAssembly +- Completed reload, in 2.796 seconds +ERROR: Shader Sprites/Default shader is not supported on this GPU (none of subshaders/fallbacks are suitable) +ERROR: Shader Sprites/Mask shader is not supported on this GPU (none of subshaders/fallbacks are suitable) +ERROR: Shader Legacy Shaders/VertexLit shader is not supported on this GPU (none of subshaders/fallbacks are suitable) +WARNING: Shader Unsupported: 'Standard (Specular setup)' - All subshaders removed +WARNING: Shader Did you use #pragma only_renderers and omit this platform? +WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off? +ERROR: Shader Standard (Specular setup) shader is not supported on this GPU (none of subshaders/fallbacks are suitable) +WARNING: Shader Unsupported: 'Standard (Specular setup)' - All subshaders removed +WARNING: Shader Did you use #pragma only_renderers and omit this platform? +WARNING: Shader If 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