cosmetics

duckietown · Nov 15, 2020 · 501a9d6 · 501a9d6
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diff --git a/book/AIDO/31_task_embodied_strategies/36_rl_baseline.md b/book/AIDO/31_task_embodied_strategies/36_rl_baseline.md
@@ -53,29 +53,29 @@ The previous uses the model that is included in the baseline repository. You are
 To do so:
 
 
- 1. Change into the directory:
+Change into the directory:
 
-        $ cd challenge-aido_LF-baseline-RL-sim-pytorch
+    $ cd challenge-aido_LF-baseline-RL-sim-pytorch
 
- 2. Install this package:
+Install this package:
 
-        $ pip3 install -e . 
+    $ pip3 install -e . 
 
 and the `gym-duckietown` package:
 
-        $ pip3 install -e git://github.com/duckietown/gym-duckietown.git@daffy#egg=gym-duckietown
+    $ pip3 install -e git://github.com/duckietown/gym-duckietown.git@daffy#egg=gym-duckietown
         
 Note: Depending on your configuration, you might need to use pip instead of pip3
 
         
- 3.  Change into the `duckietown_rl` directory and run the training script
+ Change into the `duckietown_rl` directory and run the training script
 
-        $ cd duckietown_rl
-        $ python3 -m scripts.train_cnn.py --seed 123
+    $ cd duckietown_rl
+    $ python3 -m scripts.train_cnn.py --seed 123
         
- 4.  When it finishes, try it out (make sure you pass in the same seed as the one passed to the training script)
+ When it finishes, try it out (make sure you pass in the same seed as the one passed to the training script)
 
-        $ python3 -m scripts.test_cnn.py --seed 123
+    $ python3 -m scripts.test_cnn.py --seed 123
         
 
 

diff --git a/book/AIDO/31_task_embodied_strategies/42_rpl_baseline.md b/book/AIDO/31_task_embodied_strategies/42_rpl_baseline.md
@@ -32,11 +32,11 @@ Here's a few pointers:
 
 Clone [this repo](https://github.com/duckietown/challenge-aido_LF-baseline-RPL-ros):
 
-        $ git clone  https://github.com/duckietown/challenge-aido_LF-baseline-RPL-ros.git
+    $ git clone  https://github.com/duckietown/challenge-aido_LF-baseline-RPL-ros.git
 
 Change into the directory:
 
-        $ cd challenge-aido_LF-baseline-RPL-ros
+    $ cd challenge-aido_LF-baseline-RPL-ros
 
 Test the submission, either locally with:
 
@@ -75,18 +75,18 @@ The final docker container then runs the simulator and the agent in parallel, al
 
 From the ` challenge-aido_LF-baseline-RPL-ros` directory, change into the `local_dev` directory:
 
-        $ cd local_dev
+    $ cd local_dev
 
 and open the `args.py` file.
 This is how you will control the training and testing in this repo. For now, just change the `--test` argument to `default=False`. Then, we can train with:
 
-        $ make run
+    $ make run
         
 As mentioned [](#rlp-baseline-overview), this will first build two subsequent docker images. This might take a while. Then, it will train an RL policy over the ROS stack inside Docker.
 
 When it finishes, see how it works. Simply change the `--test` flag back to `default=True` in `args.py` and test with:
 
-        $ make run
+    $ make run
         
 This will launch a simulator window on your host machine for you to view how your agent performs. You should see something like this: