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fixed the doc according to the issue #924

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Merged
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Jun 27, 2018
Merged

fixed the doc according to the issue #924

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2 changes: 1 addition & 1 deletion docs/Basic-Guide.md
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Expand Up @@ -17,7 +17,7 @@ In order to use ML-Agents within Unity, you need to change some Unity settings f
(**PC, Mac and Linux Standalone**, **iOS** or **Android**):
1. Option the **Other Settings** section.
2. Select **Scripting Runtime Version** to
**Experimental (.NET 4.6 Equivalent)**
**Experimental (.NET 4.6 Equivalent or .NET 4.x Equivalent)**
3. In **Scripting Defined Symbols**, add the flag `ENABLE_TENSORFLOW`.
After typing in the flag name, press Enter.
6. Go to **File** > **Save Project**
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2 changes: 1 addition & 1 deletion docs/FAQ.md
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Expand Up @@ -3,7 +3,7 @@

### Scripting Runtime Environment not setup correctly

If you haven't switched your scripting runtime version from .NET 3.5 to .NET 4.6, you will see such error message:
If you haven't switched your scripting runtime version from .NET 3.5 to .NET 4.6 or .NET 4.x, you will see such error message:

```
error CS1061: Type `System.Text.StringBuilder' does not contain a definition for `Clear' and no extension method `Clear' of type `System.Text.StringBuilder' could be found. Are you missing an assembly reference?
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2 changes: 1 addition & 1 deletion docs/Training-ML-Agents.md
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Expand Up @@ -68,7 +68,7 @@ The training config file, `trainer_config.yaml` specifies the training method, t
| batches_per_epoch | In imitation learning, the number of batches of training examples to collect before training the model.| BC |
| beta | The strength of entropy regularization.| PPO, BC |
| brain\_to\_imitate | For imitation learning, the name of the GameObject containing the Brain component to imitate. | BC |
| buffer_size | The number of experiences to collect before updating the policy model. | PPO, BC |
| buffer_size | The number of experiences to collect before updating the policy model. | PPO |
| curiosity\_enc\_size | The size of the encoding to use in the forward and inverse models in the Curioity module. | PPO |
| curiosity_strength | Magnitude of intrinsic reward generated by Intrinsic Curiosity Module. | PPO |
| epsilon | Influences how rapidly the policy can evolve during training.| PPO, BC |
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