SoccerTwos 4090

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: ShreyasM/SoccerTwos4090
+ 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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+default_settings: null
+behaviors:
+ SoccerTwos:
+ trainer_type: poca
+ hyperparameters:
+ batch_size: 8192
+ buffer_size: 20480
+ learning_rate: 0.0003
+ beta: 0.005
+ epsilon: 0.2
+ lambd: 0.95
+ num_epoch: 5
+ learning_rate_schedule: constant
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+ epsilon_schedule: constant
+ checkpoint_interval: 500000
+ network_settings:
+ normalize: false
+ hidden_units: 2048
+ num_layers: 15
+ vis_encode_type: simple
+ memory: null
+ goal_conditioning_type: hyper
+ deterministic: false
+ reward_signals:
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+ 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:
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+ 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: ./trained-envs-executables/SoccerTwos/SoccerTwos.x86_64
+ env_args: null
+ base_port: 5005
+ num_envs: 1
+ num_areas: 1
+ seed: -1
+ max_lifetime_restarts: 10
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+ 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

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