- RL Study Notes: SA and SGD
A review of Stochastic Approximation and the Robbins-Monro algorithm, detailing the evolution, convergence properties, and sampling differences of SGD.
7 min English - RL Study Notes: Monte Carlo Methods
RL Monte Carlo methods: MC Basic, Exploring Starts, GPI, and epsilon-Greedy for model-free optimization.
3 min English - RL Study Notes: Value Iteration and Policy Iteration
Analyzes Value & Policy Iteration, showing how Truncated PI unifies them via evaluation steps.
3 min English - RL Study Notes: Bellman Optimality Equation
Derives Bellman Optimality and fixed-point properties. Analyzes Value Iteration (contraction mapping) and how models/rewards determine the optimal policy.
4 min English - RL Study Notes: The Bellman Equation
A detailed overview of State Value and Action Value definitions, including the derivation of the Bellman Expectation Equation and its matrix representation.
5 min English - RL Study Notes: Basic Concepts
A summary of core definitions in Reinforcement Learning (State, Action, Reward) and the elements of Markov Decision Processes (MDP).
4 min en