Commit b80b792e authored by Benjamin Beyret's avatar Benjamin Beyret
Browse files

add links to curriculum documentation

parent 99a6e824
......@@ -172,6 +172,9 @@ features with the agent's frames in order to have frames in line with the config
## Version History
-v1.1.0
- Add curriculum learning to `animalai-train` to use yaml configurations
- v1.0.5
- ~~Adds customisable resolution during evaluation~~ (removed, evaluation is only `84x84`)
- Update `animalai-train` to tf 1.14 to fix `gin` broken dependency
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......@@ -5,6 +5,7 @@ You can find here the following documentation:
- [The quickstart guide](quickstart.md)
- [How to design configuration files](configFile.md)
- [How training works](training.md)
- [Add a curriculum to your training using animalai-train](curriculum.md)
- [All the objects you can include in the arenas as well as their specifications](definitionsOfObjects.md)
- [How to submit your agent](submission.md)
- [A guide to train on AWS](cloudTraining.md)
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......@@ -30,7 +30,7 @@ adding one more wall at each level. Below are samples from the 6 different level
:--------------------:|:-------------------:|:-------------------:
![](Curriculum/3.png) |![](Curriculum/4.png)|![](Curriculum/5.png)|
To produce such a curriculum, we define the meta-curriculum as the following `json` file:
To produce such a curriculum, we define the meta-curriculum in the following `json` format:
```
{
......@@ -74,7 +74,7 @@ except for the `configuration_files`. From the ml-agents documentation:
cumulative reward of the last `100` episodes exceeds the current threshold.
The mean reward logged to the console is dictated by the `summary_freq`
parameter in the
[trainer configuration file](Training-ML-Agents.md#training-config-file).
[trainer configuration file](../examples/configs/trainer_config.yaml).
* `signal_smoothing` (true/false) - Whether to weight the current progress
measure by previous values.
* If `true`, weighting will be 0.75 (new) 0.25 (old).
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