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poca-SoccerTwos-v3

Reinforcement Learning
ml-agents
TensorBoard
ONNX
unity-ml-agents
deep-reinforcement-learning
ML-Agents-SoccerTwos
Model card Files Metrics Community
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  1. .ipynb_checkpoints/configuration-checkpoint.yaml +82 -0
  2. README.md +31 -0
  3. SoccerTwos.onnx +3 -0
  4. SoccerTwos/SoccerTwos-1499745.onnx +3 -0
  5. SoccerTwos/SoccerTwos-1499745.pt +3 -0
  6. SoccerTwos/SoccerTwos-14999940.onnx +3 -0
  7. SoccerTwos/SoccerTwos-14999940.pt +3 -0
  8. SoccerTwos/SoccerTwos-15499996.onnx +3 -0
  9. SoccerTwos/SoccerTwos-15499996.pt +3 -0
  10. SoccerTwos/SoccerTwos-1595356.pt +3 -0
  11. SoccerTwos/SoccerTwos-15999976.onnx +3 -0
  12. SoccerTwos/SoccerTwos-15999976.pt +3 -0
  13. SoccerTwos/SoccerTwos-16499778.onnx +3 -0
  14. SoccerTwos/SoccerTwos-16499778.pt +3 -0
  15. SoccerTwos/SoccerTwos-16999824.onnx +3 -0
  16. SoccerTwos/SoccerTwos-16999824.pt +3 -0
  17. SoccerTwos/SoccerTwos-17090576.pt +3 -0
  18. SoccerTwos/SoccerTwos-23999906.onnx +3 -0
  19. SoccerTwos/SoccerTwos-23999906.pt +3 -0
  20. SoccerTwos/SoccerTwos-24499982.onnx +3 -0
  21. SoccerTwos/SoccerTwos-24499982.pt +3 -0
  22. SoccerTwos/SoccerTwos-24999988.onnx +3 -0
  23. SoccerTwos/SoccerTwos-24999988.pt +3 -0
  24. SoccerTwos/SoccerTwos-25499962.onnx +3 -0
  25. SoccerTwos/SoccerTwos-25499962.pt +3 -0
  26. SoccerTwos/SoccerTwos-25999908.onnx +3 -0
  27. SoccerTwos/SoccerTwos-25999908.pt +3 -0
  28. SoccerTwos/SoccerTwos-265576.pt +3 -0
  29. SoccerTwos/SoccerTwos-28499920.onnx +3 -0
  30. SoccerTwos/SoccerTwos-28499920.pt +3 -0
  31. SoccerTwos/SoccerTwos-28999916.onnx +3 -0
  32. SoccerTwos/SoccerTwos-28999916.pt +3 -0
  33. SoccerTwos/SoccerTwos-29499998.onnx +3 -0
  34. SoccerTwos/SoccerTwos-29499998.pt +3 -0
  35. SoccerTwos/SoccerTwos-29999984.onnx +3 -0
  36. SoccerTwos/SoccerTwos-29999984.pt +3 -0
  37. SoccerTwos/SoccerTwos-30000116.onnx +3 -0
  38. SoccerTwos/SoccerTwos-30000116.pt +3 -0
  39. SoccerTwos/SoccerTwos-499668.onnx +3 -0
  40. SoccerTwos/SoccerTwos-499668.pt +3 -0
  41. SoccerTwos/SoccerTwos-999212.onnx +3 -0
  42. SoccerTwos/SoccerTwos-999212.pt +3 -0
  43. SoccerTwos/checkpoint.pt +3 -0
  44. SoccerTwos/events.out.tfevents.1683117618.9f26938533b1.907.0 +3 -0
  45. SoccerTwos/events.out.tfevents.1683125463.9f26938533b1.1483.0 +3 -0
  46. SoccerTwos/events.out.tfevents.1683142932.9f26938533b1.3831.0 +3 -0
  47. SoccerTwos/events.out.tfevents.1683142963.9f26938533b1.3949.0 +3 -0
  48. SoccerTwos/events.out.tfevents.1683198603.9f26938533b1.9901.0 +3 -0
  49. SoccerTwos/events.out.tfevents.1683211534.9f26938533b1.11694.0 +3 -0
  50. SoccerTwos/events.out.tfevents.1683274987.9f26938533b1.49.0 +3 -0
.ipynb_checkpoints/configuration-checkpoint.yaml ADDED
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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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+ 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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+ even_checkpoints: false
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+ max_steps: 5000000
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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: /app/ml-agents/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
README.md ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: WilliamADSP/poca-SoccerTwos-v3
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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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