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Aug 29, 2018
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Replacing training screenshots with updated markdown. #1152

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Replacing training screenshots with updated markdown.
Aug 29, 2018
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Changing bash prompt to be simpler.
Aug 29, 2018
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87 changes: 85 additions & 2 deletions docs/Basic-Guide.md
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Expand Up @@ -109,15 +109,98 @@ perform training. Please refer to [this
page](Learning-Environment-Executable.md) for instructions on how to build and
use an executable.

![Training command example](images/training-command-example.png)
```console
ml-agents$ mlagents-learn config/trainer_config.yaml --run-id=first-run --train


▄▄▄▓▓▓▓
╓▓▓▓▓▓▓█▓▓▓▓▓
,▄▄▄m▀▀▀' ,▓▓▓▀▓▓▄ ▓▓▓ ▓▓▌
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▄▓▓▓▀ ▄▓▓▀ ▐▓▓▌ ▓▓▌ ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌ ╒▓▓▌
▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓ ▓▀ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▄ ▓▓▌
▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄ ▓▓ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▐▓▓
^█▓▓▓ ▀▓▓▄ ▐▓▓▌ ▓▓▓▓▄▓▓▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▓▄ ▓▓▓▓`
'▀▓▓▓▄ ^▓▓▓ ▓▓▓ └▀▀▀▀ ▀▀ ^▀▀ `▀▀ `▀▀ '▀▀ ▐▓▓▌
▀▀▀▀▓▄▄▄ ▓▓▓▓▓▓, ▓▓▓▓▀
`▀█▓▓▓▓▓▓▓▓▓▌
¬`▀▀▀█▓


INFO:mlagents.learn:{'--curriculum': 'None',
'--docker-target-name': 'Empty',
'--env': 'None',
'--help': False,
'--keep-checkpoints': '5',
'--lesson': '0',
'--load': False,
'--no-graphics': False,
'--num-runs': '1',
'--run-id': 'first-run',
'--save-freq': '50000',
'--seed': '-1',
'--slow': False,
'--train': True,
'--worker-id': '0',
'<trainer-config-path>': 'config/trainer_config.yaml'}
```

**Note**: If you're using Anaconda, don't forget to activate the ml-agents
environment first.

If `mlagents-learn` runs correctly and starts training, you should see something
like this:

![Training running](images/training-running.png)
```console
INFO:mlagents.envs:Start training by pressing the Play button in the Unity Editor.
INFO:mlagents.envs:
'Ball3DAcademy' started successfully!
Unity Academy name: Ball3DAcademy
Number of Brains: 1
Number of External Brains : 1
Reset Parameters :

Unity brain name: Ball3DBrain
Number of Visual Observations (per agent): 0
Vector Observation space size (per agent): 8
Number of stacked Vector Observation: 1
Vector Action space type: continuous
Vector Action space size (per agent): [2]
Vector Action descriptions: ,
INFO:mlagents.envs:Hyperparameters for the PPO Trainer of brain Ball3DBrain:
batch_size: 64
beta: 0.001
buffer_size: 12000
epsilon: 0.2
gamma: 0.995
hidden_units: 128
lambd: 0.99
learning_rate: 0.0003
max_steps: 5.0e4
normalize: True
num_epoch: 3
num_layers: 2
time_horizon: 1000
sequence_length: 64
summary_freq: 1000
use_recurrent: False
graph_scope:
summary_path: ./summaries/first-run-0
memory_size: 256
use_curiosity: False
curiosity_strength: 0.01
curiosity_enc_size: 128
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training.
```

### After training

Expand Down
95 changes: 92 additions & 3 deletions docs/Learning-Environment-Executable.md
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Expand Up @@ -100,18 +100,107 @@ For example, if you are training with a 3DBall executable you exported to the
ml-agents/python directory, run:

```sh
mlagents-learn config/trainer_config.yaml --env=3DBall --run-id=firstRun --train
mlagents-learn config/trainer_config.yaml --env=3DBall --run-id=first-run --train
```

![Training command example](images/training-command-example.png)
And you should see something like

```console
ml-agents$ mlagents-learn config/trainer_config.yaml --env=3DBall --run-id=first-run --train


▄▄▄▓▓▓▓
╓▓▓▓▓▓▓█▓▓▓▓▓
,▄▄▄m▀▀▀' ,▓▓▓▀▓▓▄ ▓▓▓ ▓▓▌
▄▓▓▓▀' ▄▓▓▀ ▓▓▓ ▄▄ ▄▄ ,▄▄ ▄▄▄▄ ,▄▄ ▄▓▓▌▄ ▄▄▄ ,▄▄
▄▓▓▓▀ ▄▓▓▀ ▐▓▓▌ ▓▓▌ ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌ ╒▓▓▌
▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓ ▓▀ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▄ ▓▓▌
▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄ ▓▓ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▐▓▓
^█▓▓▓ ▀▓▓▄ ▐▓▓▌ ▓▓▓▓▄▓▓▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▓▄ ▓▓▓▓`
'▀▓▓▓▄ ^▓▓▓ ▓▓▓ └▀▀▀▀ ▀▀ ^▀▀ `▀▀ `▀▀ '▀▀ ▐▓▓▌
▀▀▀▀▓▄▄▄ ▓▓▓▓▓▓, ▓▓▓▓▀
`▀█▓▓▓▓▓▓▓▓▓▌
¬`▀▀▀█▓


INFO:mlagents.learn:{'--curriculum': 'None',
'--docker-target-name': 'Empty',
'--env': '3DBall',
'--help': False,
'--keep-checkpoints': '5',
'--lesson': '0',
'--load': False,
'--no-graphics': False,
'--num-runs': '1',
'--run-id': 'firstRun',
'--save-freq': '50000',
'--seed': '-1',
'--slow': False,
'--train': True,
'--worker-id': '0',
'<trainer-config-path>': 'config/trainer_config.yaml'}
```

**Note**: If you're using Anaconda, don't forget to activate the ml-agents
environment first.

If `mlagents-learn` runs correctly and starts training, you should see something
like this:

![Training running](images/training-running.png)
```console
CrashReporter: initialized
Mono path[0] = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/Resources/Data/Managed'
Mono config path = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/MonoBleedingEdge/etc'
INFO:mlagents.envs:
'Ball3DAcademy' started successfully!
INFO:mlagents.envs:
'Ball3DAcademy' started successfully!
Unity Academy name: Ball3DAcademy
Number of Brains: 1
Number of External Brains : 1
Reset Parameters :

Unity brain name: Ball3DBrain
Number of Visual Observations (per agent): 0
Vector Observation space size (per agent): 8
Number of stacked Vector Observation: 1
Vector Action space type: continuous
Vector Action space size (per agent): [2]
Vector Action descriptions: ,
INFO:mlagents.envs:Hyperparameters for the PPO Trainer of brain Ball3DBrain:
batch_size: 64
beta: 0.001
buffer_size: 12000
epsilon: 0.2
gamma: 0.995
hidden_units: 128
lambd: 0.99
learning_rate: 0.0003
max_steps: 5.0e4
normalize: True
num_epoch: 3
num_layers: 2
time_horizon: 1000
sequence_length: 64
summary_freq: 1000
use_recurrent: False
graph_scope:
summary_path: ./summaries/first-run-0
memory_size: 256
use_curiosity: False
curiosity_strength: 0.01
curiosity_enc_size: 128
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training.
INFO:mlagents.trainers: first-run-0: Ball3DBrain: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training.
```

You can press Ctrl+C to stop the training, and your trained model will be at
`models/<run-identifier>/<env_name>_<run-identifier>.bytes`, which corresponds
Expand Down
3 changes: 2 additions & 1 deletion docs/Migrating.md
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Expand Up @@ -20,7 +20,8 @@

* In order to run a training session, you can now use the command
`mlagents-learn` instead of `python3 learn.py` after installing the `mlagents`
packages. This change is documented [here](Training-ML-Agents.md#training-with-mlagents-learn).
packages. This change is documented
[here](Training-ML-Agents.md#training-with-mlagents-learn).
* It is now required to specify the path to the yaml trainer configuration file
when running `mlagents-learn`. For example, see
[trainer_config.yaml](../config/trainer_config.yaml).
Expand Down
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