Explores the connection between classical derivative pricing (HJB) and reinforcement learning, highlighting
trajectory-wise vs. average perspectives in risk-neutral settings. Covers high-dimensional numerical methods, from
adaptive Euler schemes to Longstaff‑Schwartz and neural network regressions. Examines market making and
liquidity through optimal control, learning by trading, and mean-field approaches for execution and transaction cost
analysis.
