readme

rl/README.md

 # Reinforcment Learning libary for training the RC Car
 
 A small libary for training the RC Car with reinforcement learning algorithm.
-The two algorithm Proximal Policy Optimization PPO <cite>[PPO][1]</cite> and Soft Actor Critic <cite>[SAC][2]</cite> are implemented.
+The two algorithm Proximal Policy Optimization <cite>[PPO][1]</cite> and Soft Actor Critic <cite>[SAC][2]</cite> are implemented.
 A new experiment can be started by running train.py
 
-`python train.py --config train_ppo.yaml  --log-dir runs/ppo --exp-name 'experiment_name' --track  --port 8001 --cuda`
+```
+python train.py --config train_ppo.yaml  --log-dir runs/ppo --exp-name 'experiment_name' --track  --port 8001 --cuda
+```
 
-for more options run
-
-`python train.py --help`
+for more options run `python train.py --help`
 
 The environments are saved in [../simulations](../simulations)
 
 For evaluating a trained model run
 
-`python test.py --log-dir ./runs/ppo/exp_name --n-episodes 10 --env ../simulations/path_to_env`
-
-More options with
+```
+python test.py --log-dir ./runs/ppo/exp_name --n-episodes 10 --env ../simulations/path_to_env
+```
 
-`python test.py --help`
+More options with `python test.py --help`
 
 The default model is best_model.pth with `--model-filename` another model can be chosen.