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results/SoccerTwos/README.md

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----
-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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
-
-  We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
-  - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
-  browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
-  - A *longer tutorial* to understand how works ML-Agents:
-  https://huggingface.co/learn/deep-rl-course/unit5/introduction
-
-  ### Resume the training
-  ```bash
-  mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
-  ```
-
-  ### Watch your Agent play
-  You can watch your agent **playing directly in your browser**
-
-  1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
-  2. Step 1: Find your model_id: SSGoatt/poca-SoccerTwos
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀
-  

results/SoccerTwos/SoccerTwos.onnx

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results/SoccerTwos/config.json

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results/SoccerTwos/configuration.yaml

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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
-    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: 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/SoccerTwos.exe
-  env_args: null
-  base_port: 5005
-  num_envs: 1
-  num_areas: 1
-  timeout_wait: 60
-  seed: -1
-  max_lifetime_restarts: 10
-  restarts_rate_limit_n: 1
-  restarts_rate_limit_period_s: 60
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-  height: 84
-  quality_level: 5
-  time_scale: 20
-  target_frame_rate: -1
-  capture_frame_rate: 60
-  no_graphics: true
-  no_graphics_monitor: false
-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:
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-debug: false

results/SoccerTwos/run_logs/Player-0.log

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results/SoccerTwos/run_logs/timers.json

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results/SoccerTwos/run_logs/training_status.json

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