In this exercise we will revisit the included racetrack_environment to have a look at temporal difference (TD) algorithms.

1. policy evaluation using TD learning
2. on-policy epsilon-greedy control using TD learning
3. off-policy epsilon-greedy control using TD learning → Q-learning
4. using double Q-learning in stochastic environments