For this exercise we will have a look at n-step methods, which are the generalization of Monte-Carlo and TD learning algorithms. The environment under examination is given by the inverted pendulum, which is a popular system for toy examples of control theory.
- discretization of continuous state spaces in order to make corresponding systems available for tabular RL algorithms
- on-policy epsilon-greedy control using n-step Sarsa
- off-policy epsilon-greedy control using tree backups
- hyperparameter optimization for the Q(σ) algorithm