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README.md

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+
+---
+ 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 <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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
+ 2. Step 1: Write your model_id: atorre/poca-SoccerTwos-100M
+ 3. Step 2: Select your *.nn /*.onnx file
+ 4. Click on Watch the agent play 👀
+ 

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config.json

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configuration.yaml

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+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: linear
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+ epsilon_schedule: linear
+ checkpoint_interval: 100000
+ network_settings:
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+ num_layers: 2
+ vis_encode_type: simple
+ memory: null
+ goal_conditioning_type: hyper
+ deterministic: false
+ reward_signals:
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+ summary_freq: 10000
+ threaded: false
+ self_play:
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+ window: 10
+ play_against_latest_model_ratio: 0.5
+ initial_elo: 1200.0
+ behavioral_cloning: null
+env_settings:
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+ env_args: null
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+ num_envs: 1
+ num_areas: 1
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+ height: 84
+ quality_level: 5
+ time_scale: 20.0
+ target_frame_rate: -1
+ capture_frame_rate: 60
+ no_graphics: true
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+ load_model: false
+ resume: true
+ force: false
+ train_model: false
+ inference: false
+ results_dir: results
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