Welcome to Decision Making and Reinforcement Learning! During this week, Professor Tony Dear provides an overview of the course. You will also view guidelines to support your learning journey towards modeling sequential decision problems and implementing reinforcement learning algorithms.

Welcome to week 2! This week, we will learn about multi-armed bandit problems, a type of optimization problem in which the algorithm balances exploration and exploitation to maximize rewards. Topics include action values and sample averaging estimation, 𝜀-greedy action selection, and the upper confidence bound. You could post in the discussion forum if you need assistance on the quiz and assignment.

Welcome to week 3! This week, we will focus on the basics of the Markov decision process, including rewards, utilities, discounting, policies, value functions, and Bellman equations. You will model sequential decision problems, understand the impact of rewards and discount factors on outcomes, define policies and value functions, and write Bellman equations for optimal solutions. You could post in the discussion forum if you need assistance on the quiz and assignment.

Welcome to week 4! This week, we will cover dynamic programming algorithms for solving Markov decision processes (MDPs). Topics include value iteration and policy iteration, nonlinear Bellman equations, complexity and convergence, and a comparison of the two approaches.You could post in the discussion forum if you need assistance on the quiz and assignment.

Welcome to week 5! This week, we will go through topics on partial observability and POMDPs, belief states, representation as belief MDPs, and online planning in MDPs and POMDPs. You will also apply your knowledge to update the belief state and employ a belief transition function to calculate state values. You could post in the discussion forum if you need assistance on the quiz and assignment.

Welcome to week 6! This week, we will introduce Monte Carlo methods, and cover topics related to state value estimation using sample averaging and Monte Carlo prediction, state-action values and epsilon-greedy policies, and importance sampling for off-policy vs on-policy Monte Carlo control. You will learn to estimate state values, state-action values, use importance sampling, and implement off-policy Monte Carlo control for optimal policy learning. You could post in the discussion forum if you need assistance on the quiz and assignment.

Welcome to week 7! This week, we will cover topics related to temporal difference learning for prediction, TD batch methods, SARSA for on-policy control, and Q-learning for off-policy control. You will learn to implement TD prediction, TD batch and offline methods, SARSA and Q-learning, and compare on-policy vs off-policy TD learning. You will then apply your knowledge in solving a Tic-tac-toe programming assignment.You could post in the discussion forum if you need assistance on the quiz and assignment.

Welcome to week 8! This module covers n-step temporal difference prediction, n-step SARSA (on-policy and off-policy), model-based RL with Dyna-Q, and function approximation. You will be prepared to implement n-step TD learning, n-step SARSA, Dyna-Q for model-based learning, and use function approximation for reinforcement learning. You will apply your knowledge in the Frozen Lake programming environment. You could post in the discussion forum if you need assistance on the quiz and assignment.