9 posts with this topic
AresSim is a 32×32 Mars survival grid I built from scratch to test RL algorithms and LLM agents: a rover explores terrain, extracts ice, and keeps a 5×5 colony pad alive under weather, battery drain, in seed-reproducible worlds.
A deep dive into planning and learning with tabular methods for Reinforcement Learning, focusing on value iteration and policy iteration.
A deep dive into Temporal Difference (TD) Learning for Reinforcement Learning, an idea that forms basis for modern deep reinforcement learning algorithms
A deep dive into Monte Carlo methods for Reinforcement Learning, focusing on learning optimal strategies from raw experience without a model.
Introduction to Dynamic Programming: Policy Evaluation, Policy Iteration, Value Iteration and Generalized Policy Iteration
Introduction to sequential decision-making in reinforcement learning, including agent–environment interaction, rewards, returns, value functions, and Bellman equations with intuitive example.
Understanding the multi-armed bandit problem and how to solve it using reinforcement learning alongwith interactive demo. Chapter 3 of Reinforcement Learning: An Introduction by Sutton and Barto
Understanding how different algorithms plays Tic-Tac-Toe and why RL wins at it. Interactive demo included!
A comprehensive introduction to reinforcement learning from the book : Reinforcement Learning: An Introduction by Sutton and Barto
