add links to curriculum documentation

README.md

@@ -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

documentation/README.md

@@ -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)

documentation/curriculum.md

@@ -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).