clean notebooks outputs

README.md

@@ -20,7 +20,7 @@ We ran a competition using this environment and the associated tests, more detai
 The environment is built using [Unity ml-agents](https://github.com/Unity-Technologies/ml-agents/tree/master/docs) and contains an agent enclosed in a fixed sized arena. Objects can spawn in this arena, including positive 
 and negative rewards (green, yellow and red spheres) that the agent must obtain (or avoid). All of the hidden tests that will appear in the competition are made using the objects in the training environment. 
 
-To get started install the requirements below, and then follow the [Quick Start Guide](documentation/quickstart.md). 
+To get started install the requirements below, and then follow the jupyter notebook tutorials in the [examples folder](examples). 
 More in depth documentation can be found on the [Documentation Page](documentation/README.md).
 
 ## Development Blog

examples/README.md

@@ -11,12 +11,16 @@ Then you can start a jupyter notebook by running `jupyter notebook` from your te
 
 ## Designing arenas
 
+For a tutorial on how to design experiments and training configurations we provide a [jupyter notebook](environment_tutorial.ipynb)
+
 You can use `load_config_and_play.py` to visualize a `yml` configuration for an environment arena. Make sure `animalai` 
 is [installed](../README.md#requirements) and run `python load_config_and_play.py your_configuration_file.yml` which will open the environment in 
 play mode (control with W,A,S,D or the arrows), close the environment by pressing CTRL+C in the terminal.
 
 ## Animalai-train examples
 
+You will find a training tutorial in this [jupyter notebook](training_tutorial.ipynb)
+
 We provide two scripts which show how to use `animalai_train` to train agents:
 - `train_ml_agents.py` uses ml-agents' PPO implementation (or SAC) and can run multiple environments in parralel to speed up 
 the training process

examples/environment_tutorial.ipynb

@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -68,7 +68,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }