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JYC333
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poca-SoccerTwos-v1

Reinforcement Learning
ml-agents
TensorBoard
ONNX
unity-ml-agents
deep-reinforcement-learning
ML-Agents-SoccerTwos
Model card Files
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  1. README.md +31 -0
  2. SoccerTwos.onnx +3 -0
  3. SoccerTwos/SoccerTwos-40499932.onnx +3 -0
  4. SoccerTwos/SoccerTwos-40499932.pt +3 -0
  5. SoccerTwos/SoccerTwos-40999958.onnx +3 -0
  6. SoccerTwos/SoccerTwos-40999958.pt +3 -0
  7. SoccerTwos/SoccerTwos-41499965.onnx +3 -0
  8. SoccerTwos/SoccerTwos-41499965.pt +3 -0
  9. SoccerTwos/SoccerTwos-41999990.onnx +3 -0
  10. SoccerTwos/SoccerTwos-41999990.pt +3 -0
  11. SoccerTwos/SoccerTwos-42499934.onnx +3 -0
  12. SoccerTwos/SoccerTwos-42499934.pt +3 -0
  13. SoccerTwos/SoccerTwos-48999902.onnx +3 -0
  14. SoccerTwos/SoccerTwos-48999902.pt +3 -0
  15. SoccerTwos/SoccerTwos-49499980.onnx +3 -0
  16. SoccerTwos/SoccerTwos-49499980.pt +3 -0
  17. SoccerTwos/SoccerTwos-49999994.onnx +3 -0
  18. SoccerTwos/SoccerTwos-49999994.pt +3 -0
  19. SoccerTwos/SoccerTwos-50000010.onnx +3 -0
  20. SoccerTwos/SoccerTwos-50000010.pt +3 -0
  21. SoccerTwos/checkpoint.pt +3 -0
  22. SoccerTwos/events.out.tfevents.1675620254.DESKTOP-G2A94QN.1998.0 +3 -0
  23. config.json +1 -0
  24. configuration.yaml +82 -0
  25. run_logs/Player-0.log +228 -0
  26. run_logs/timers.json +54 -0
  27. run_logs/training_status.json +66 -0
README.md ADDED
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+
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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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+
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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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+
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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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+
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+
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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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+
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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: JYC333/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
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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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+ checkpoint_interval: 500000
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+ network_settings:
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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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+ 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: 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/SoccerTwos/SoccerTwos.x86_64
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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: true
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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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