PPO

Learning objectives Train a PPO agent on 10 different random seeds and collect final returns (or mean return over the last N episodes) for each seed. Compute the mean and standard deviation of these returns and report them (e.g. “mean ± std”). Compute stratified confidence intervals (e.g. using the rliable library or similar) so that intervals account for both within-run and across-run variance. Interpret the results: what does the interval tell us about the agent’s performance and reliability? Why is reporting only mean ± std over seeds often insufficient? Relate evaluation practice to robot navigation, healthcare, and trading where reliable performance estimates matter. Concept and real-world RL ...

Use this quiz after completing Volumes 3–5 (or the Phase 4 coding challenges). If you can answer at least 9 of 12 correctly, you are ready for Phase 5 and Volume 6. 1. Function approximation Q: Why is function approximation necessary in RL for large or continuous state spaces? Answer Tabular methods store one value per state (or state-action); the number of states can be huge or infinite. Function approximation uses a parameterized function (e.g. neural network) so a fixed number of parameters represent values for all states and generalize from seen to unseen states. ...