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Test training huggy.

Browse files
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README.md CHANGED
@@ -1,31 +1,35 @@
1
-
2
  ---
3
- tags:
4
- - unity-ml-agents
5
- - ml-agents
6
- - deep-reinforcement-learning
7
- - reinforcement-learning
8
- - ML-Agents-Huggy
9
- library_name: ml-agents
10
  ---
11
-
12
  # **ppo** Agent playing **Huggy**
13
- This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
14
-
 
15
  ## Usage (with ML-Agents)
16
- The Documentation: https://github.com/huggingface/ml-agents#get-started
17
- We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
18
 
 
 
 
 
 
19
 
20
  ### Resume the training
21
- ```
22
  mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
23
  ```
 
24
  ### Watch your Agent play
25
- You can watch your agent **playing directly in your browser:**.
26
-
27
- 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
28
- 2. Step 1: Write your model_id: Lpremier/ppo-Huggy
29
  3. Step 2: Select your *.nn /*.onnx file
30
  4. Click on Watch the agent play 👀
31
 
 
 
1
  ---
2
+ library_name: ml-agents
3
+ tags:
4
+ - Huggy
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - ML-Agents-Huggy
 
8
  ---
9
+
10
  # **ppo** Agent playing **Huggy**
11
+ This is a trained model of a **ppo** agent playing **Huggy**
12
+ using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
13
+
14
  ## Usage (with ML-Agents)
15
+ The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
 
16
 
17
+ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
18
+ - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
19
+ browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
20
+ - A *longer tutorial* to understand how works ML-Agents:
21
+ https://huggingface.co/learn/deep-rl-course/unit5/introduction
22
 
23
  ### Resume the training
24
+ ```bash
25
  mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
26
  ```
27
+
28
  ### Watch your Agent play
29
+ You can watch your agent **playing directly in your browser**
30
+
31
+ 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
32
+ 2. Step 1: Find your model_id: Lpremier/ppo-Huggy
33
  3. Step 2: Select your *.nn /*.onnx file
34
  4. Click on Watch the agent play 👀
35
 
config.json CHANGED
@@ -1 +1 @@
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- {"default_settings": null, "behaviors": {"Huggy": {"trainer_type": "ppo", "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", "beta_schedule": "linear", "epsilon_schedule": "linear"}, "network_settings": {"normalize": true, "hidden_units": 512, "num_layers": 3, "vis_encode_type": "simple", "memory": null, "goal_conditioning_type": "hyper", "deterministic": false}, "reward_signals": {"extrinsic": {"gamma": 0.995, "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": 15, "checkpoint_interval": 200000, "max_steps": 2000000, "time_horizon": 1000, "summary_freq": 50000, "threaded": false, "self_play": null, "behavioral_cloning": null}}, "env_settings": {"env_path": "./trained-envs-executables/linux/Huggy/Huggy", "env_args": null, "base_port": 5005, "num_envs": 1, "num_areas": 1, "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, "target_frame_rate": -1, "capture_frame_rate": 60, "no_graphics": true}, "environment_parameters": null, "checkpoint_settings": {"run_id": "Huggy", "initialize_from": null, "load_model": false, "resume": false, "force": false, "train_model": false, "inference": false, "results_dir": "results"}, "torch_settings": {"device": null}, "debug": false}
 
1
+ {"default_settings": null, "behaviors": {"Huggy": {"trainer_type": "ppo", "hyperparameters": {"batch_size": 2048, "buffer_size": 20480, "learning_rate": 0.0003, "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": 200000, "network_settings": {"normalize": true, "hidden_units": 256, "num_layers": 3, "vis_encode_type": "simple", "memory": null, "goal_conditioning_type": "hyper", "deterministic": false}, "reward_signals": {"extrinsic": {"gamma": 0.995, "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": 15, "even_checkpoints": false, "max_steps": 2000000, "time_horizon": 1000, "summary_freq": 50000, "threaded": false, "self_play": null, "behavioral_cloning": null}}, "env_settings": {"env_path": "./envs/Huggy/Huggy.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, "target_frame_rate": -1, "capture_frame_rate": 60, "no_graphics": true, "no_graphics_monitor": false}, "environment_parameters": null, "checkpoint_settings": {"run_id": "Huggy", "initialize_from": null, "load_model": false, "resume": false, "force": false, "train_model": false, "inference": false, "results_dir": "results"}, "torch_settings": {"device": null}, "debug": false}
configuration.yaml CHANGED
@@ -10,12 +10,14 @@ behaviors:
10
  epsilon: 0.2
11
  lambd: 0.95
12
  num_epoch: 3
 
13
  learning_rate_schedule: linear
14
  beta_schedule: linear
15
  epsilon_schedule: linear
 
16
  network_settings:
17
  normalize: true
18
- hidden_units: 512
19
  num_layers: 3
20
  vis_encode_type: simple
21
  memory: null
@@ -35,7 +37,7 @@ behaviors:
35
  deterministic: false
36
  init_path: null
37
  keep_checkpoints: 15
38
- checkpoint_interval: 200000
39
  max_steps: 2000000
40
  time_horizon: 1000
41
  summary_freq: 50000
@@ -43,11 +45,12 @@ behaviors:
43
  self_play: null
44
  behavioral_cloning: null
45
  env_settings:
46
- env_path: ./trained-envs-executables/linux/Huggy/Huggy
47
  env_args: null
48
  base_port: 5005
49
  num_envs: 1
50
  num_areas: 1
 
51
  seed: -1
52
  max_lifetime_restarts: 10
53
  restarts_rate_limit_n: 1
@@ -60,6 +63,7 @@ engine_settings:
60
  target_frame_rate: -1
61
  capture_frame_rate: 60
62
  no_graphics: true
 
63
  environment_parameters: null
64
  checkpoint_settings:
65
  run_id: Huggy
 
10
  epsilon: 0.2
11
  lambd: 0.95
12
  num_epoch: 3
13
+ shared_critic: false
14
  learning_rate_schedule: linear
15
  beta_schedule: linear
16
  epsilon_schedule: linear
17
+ checkpoint_interval: 200000
18
  network_settings:
19
  normalize: true
20
+ hidden_units: 256
21
  num_layers: 3
22
  vis_encode_type: simple
23
  memory: null
 
37
  deterministic: false
38
  init_path: null
39
  keep_checkpoints: 15
40
+ even_checkpoints: false
41
  max_steps: 2000000
42
  time_horizon: 1000
43
  summary_freq: 50000
 
45
  self_play: null
46
  behavioral_cloning: null
47
  env_settings:
48
+ env_path: ./envs/Huggy/Huggy.x86_64
49
  env_args: null
50
  base_port: 5005
51
  num_envs: 1
52
  num_areas: 1
53
+ timeout_wait: 60
54
  seed: -1
55
  max_lifetime_restarts: 10
56
  restarts_rate_limit_n: 1
 
63
  target_frame_rate: -1
64
  capture_frame_rate: 60
65
  no_graphics: true
66
+ no_graphics_monitor: false
67
  environment_parameters: null
68
  checkpoint_settings:
69
  run_id: Huggy
run_logs/Player-0.log CHANGED
@@ -1,40 +1,17 @@
1
- Mono path[0] = '/content/ml-agents/trained-envs-executables/linux/Huggy/Huggy_Data/Managed'
2
- Mono config path = '/content/ml-agents/trained-envs-executables/linux/Huggy/Huggy_Data/MonoBleedingEdge/etc'
3
  Preloaded 'lib_burst_generated.so'
4
  Preloaded 'libgrpc_csharp_ext.x64.so'
5
- PlayerPrefs - Creating folder: /root/.config/unity3d/Hugging Face
6
- PlayerPrefs - Creating folder: /root/.config/unity3d/Hugging Face/Huggy
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- Unable to load player prefs
8
  Initialize engine version: 2021.3.14f1 (eee1884e7226)
9
- [Subsystems] Discovering subsystems at path /content/ml-agents/trained-envs-executables/linux/Huggy/Huggy_Data/UnitySubsystems
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  Forcing GfxDevice: Null
11
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  Version: NULL 1.0 [1.0]
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  Renderer: Null Device
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  Vendor: Unity Technologies
16
- ALSA lib confmisc.c:767:(parse_card) cannot find card '0'
17
- ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_card_driver returned error: No such file or directory
18
- ALSA lib confmisc.c:392:(snd_func_concat) error evaluating strings
19
- ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
20
- ALSA lib confmisc.c:1246:(snd_func_refer) error evaluating name
21
- ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
22
- ALSA lib conf.c:5220:(snd_config_expand) Evaluate error: No such file or directory
23
- ALSA lib pcm.c:2642:(snd_pcm_open_noupdate) Unknown PCM default
24
- FMOD failed to initialize the output device.: "Error initializing output device. " (60)
25
- Forced to initialize FMOD to to the device driver's system output rate 48000, this may impact performance and/or give inconsistent experiences compared to selected sample rate 48000
26
- ALSA lib confmisc.c:767:(parse_card) cannot find card '0'
27
- ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_card_driver returned error: No such file or directory
28
- ALSA lib confmisc.c:392:(snd_func_concat) error evaluating strings
29
- ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_concat returned error: No such file or directory
30
- ALSA lib confmisc.c:1246:(snd_func_refer) error evaluating name
31
- ALSA lib conf.c:4732:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
32
- ALSA lib conf.c:5220:(snd_config_expand) Evaluate error: No such file or directory
33
- ALSA lib pcm.c:2642:(snd_pcm_open_noupdate) Unknown PCM default
34
- FMOD failed to initialize the output device.: "Error initializing output device. " (60)
35
- FMOD initialized on nosound output
36
  Begin MonoManager ReloadAssembly
37
- - Completed reload, in 0.084 seconds
38
  ERROR: Shader Hidden/Universal Render Pipeline/Blit shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
39
  ERROR: Shader Hidden/Universal Render Pipeline/CopyDepth shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
40
  ERROR: Shader Hidden/Universal Render Pipeline/ScreenSpaceShadows shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
@@ -78,14 +55,14 @@ ERROR: Shader Universal Render Pipeline/Lit shader is not supported on this GPU
78
  WARNING: Shader Unsupported: 'Universal Render Pipeline/Lit' - All subshaders removed
79
  WARNING: Shader Did you use #pragma only_renderers and omit this platform?
80
  WARNING: Shader If subshaders removal was intentional, you may have forgotten turning Fallback off?
81
- UnloadTime: 0.780936 ms
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  requesting resize 84 x 84
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- Setting up 1 worker threads for Enlighten.
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  Memory Statistics:
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  [ALLOC_TEMP_TLS] TLS Allocator
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  StackAllocators :
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  [ALLOC_TEMP_MAIN]
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  Initial Block Size 4.0 MB
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  Current Block Size 4.0 MB
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  Peak Allocated Bytes 3.6 MB
@@ -93,21 +70,21 @@ Memory Statistics:
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  [ALLOC_TEMP_Loading.AsyncRead]
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  Initial Block Size 64.0 KB
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- Peak Allocated Bytes 136 B
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  Current Block Size 296.0 KB
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- Peak Allocated Bytes 211.5 KB
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@@ -120,6 +97,16 @@ Memory Statistics:
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@@ -130,12 +117,22 @@ Memory Statistics:
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