more trained model
Browse files- README.md +8 -2
- SnowballTarget.onnx +1 -1
- SnowballTarget/SnowballTarget-1000120.onnx +3 -0
- SnowballTarget/SnowballTarget-1000120.pt +3 -0
- SnowballTarget/SnowballTarget-1499968.onnx +3 -0
- SnowballTarget/SnowballTarget-1499968.pt +3 -0
- SnowballTarget/SnowballTarget-1999944.onnx +3 -0
- SnowballTarget/SnowballTarget-1999944.pt +3 -0
- SnowballTarget/SnowballTarget-2000328.onnx +3 -0
- SnowballTarget/SnowballTarget-2000328.pt +3 -0
- SnowballTarget/SnowballTarget-2499984.onnx +3 -0
- SnowballTarget/SnowballTarget-2499984.pt +3 -0
- SnowballTarget/SnowballTarget-2999960.onnx +3 -0
- SnowballTarget/SnowballTarget-2999960.pt +3 -0
- SnowballTarget/SnowballTarget-3499936.onnx +3 -0
- SnowballTarget/SnowballTarget-3499936.pt +3 -0
- SnowballTarget/SnowballTarget-3999976.onnx +3 -0
- SnowballTarget/SnowballTarget-3999976.pt +3 -0
- SnowballTarget/SnowballTarget-4059440.onnx +3 -0
- SnowballTarget/SnowballTarget-4059440.pt +3 -0
- SnowballTarget/SnowballTarget-999992.onnx +3 -0
- SnowballTarget/SnowballTarget-999992.pt +3 -0
- SnowballTarget/checkpoint.pt +2 -2
- SnowballTarget/events.out.tfevents.1759619468.222bc6b1bac0.23309.0 +3 -0
- SnowballTarget/events.out.tfevents.1759635131.74437285bc07.11580.0 +3 -0
- SnowballTarget/events.out.tfevents.1759637937.74437285bc07.23232.0 +3 -0
- SnowballTarget/events.out.tfevents.1759641721.74437285bc07.38555.0 +3 -0
- config.json +1 -1
- configuration.yaml +6 -6
- run_logs/Player-0.log +15 -18
- run_logs/timers.json +162 -162
- run_logs/training_status.json +75 -30
README.md
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@@ -14,13 +14,19 @@ tags:
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## Usage (with ML-Agents)
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The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
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### Resume the training
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```bash
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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
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-
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1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
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2. Step 1: Find your model_id: elliemci/ppo-Snowball-Target
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## Usage (with ML-Agents)
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The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
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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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- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
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browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
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- A *longer tutorial* to understand how works ML-Agents:
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https://huggingface.co/learn/deep-rl-course/unit5/introduction
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### Resume the training
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```bash
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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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1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
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2. Step 1: Find your model_id: elliemci/ppo-Snowball-Target
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SnowballTarget.onnx
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SnowballTarget/checkpoint.pt
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SnowballTarget/events.out.tfevents.1759619468.222bc6b1bac0.23309.0
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config.json
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| 1 |
-
{"default_settings": null, "behaviors": {"SnowballTarget": {"trainer_type": "ppo", "hyperparameters": {"batch_size": 128, "buffer_size": 2048, "learning_rate": 0.
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{"default_settings": null, "behaviors": {"SnowballTarget": {"trainer_type": "ppo", "hyperparameters": {"batch_size": 128, "buffer_size": 2048, "learning_rate": 0.001, "beta": 0.005, "epsilon": 0.2, "lambd": 0.95, "num_epoch": 3, "shared_critic": false, "learning_rate_schedule": "linear", "beta_schedule": "linear", "epsilon_schedule": "linear"}, "checkpoint_interval": 500000, "network_settings": {"normalize": false, "hidden_units": 256, "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": 10, "even_checkpoints": false, "max_steps": 5000000, "time_horizon": 64, "summary_freq": 10000, "threaded": true, "self_play": null, "behavioral_cloning": null}}, "env_settings": {"env_path": "training-envs-executables/linux/SnowballTarget/SnowballTarget.x86_64", "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}, "engine_settings": {"width": 84, "height": 84, "quality_level": 5, "time_scale": 20.0, "target_frame_rate": -1, "capture_frame_rate": 60, "no_graphics": true, "no_graphics_monitor": false}, "environment_parameters": null, "checkpoint_settings": {"run_id": "SnowballTarget1", "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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configuration.yaml
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hyperparameters:
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batch_size: 128
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buffer_size: 2048
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-
learning_rate: 0.
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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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@@ -14,7 +14,7 @@ behaviors:
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learning_rate_schedule: linear
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beta_schedule: linear
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epsilon_schedule: linear
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checkpoint_interval:
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network_settings:
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normalize: false
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hidden_units: 256
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init_path: null
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keep_checkpoints: 10
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even_checkpoints: false
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max_steps:
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time_horizon: 64
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summary_freq: 10000
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threaded: true
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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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run_id: SnowballTarget1
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initialize_from: null
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load_model: false
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-
resume:
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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:
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debug: false
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hyperparameters:
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batch_size: 128
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buffer_size: 2048
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+
learning_rate: 0.001
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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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learning_rate_schedule: linear
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beta_schedule: linear
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epsilon_schedule: linear
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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: 256
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init_path: null
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keep_checkpoints: 10
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even_checkpoints: false
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max_steps: 5000000
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time_horizon: 64
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summary_freq: 10000
|
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threaded: true
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width: 84
|
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height: 84
|
| 61 |
quality_level: 5
|
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time_scale: 20.0
|
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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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run_id: SnowballTarget1
|
| 70 |
initialize_from: null
|
| 71 |
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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run_logs/Player-0.log
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@@ -2,9 +2,6 @@ Mono path[0] = '/content/drive/MyDrive/ColabNotebooks/DeepRL/unity_ml_agents/ml-
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|
| 2 |
Mono config path = '/content/drive/MyDrive/ColabNotebooks/DeepRL/unity_ml_agents/ml-agents/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/MonoBleedingEdge/etc'
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Unable to load player prefs
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[Subsystems] Discovering subsystems at path /content/drive/MyDrive/ColabNotebooks/DeepRL/unity_ml_agents/ml-agents/training-envs-executables/linux/SnowballTarget/SnowballTarget_Data/UnitySubsystems
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|
| 5 |
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| 35 |
ERROR: Shader Sprites/Default shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
|
| 36 |
ERROR: Shader Sprites/Mask shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
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|
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|
| 42 |
WARNING: Shader Unsupported: 'Standard' - All subshaders removed
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WARNING: Shader Did you use #pragma only_renderers and omit this platform?
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| 44 |
WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
|
| 45 |
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requesting resize 84 x 84
|
| 48 |
Setting up 1 worker threads for Enlighten.
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|
|
|
| 50 |
[ALLOC_TEMP_TLS] TLS Allocator
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