What is the difference between value iteration and policy iteration in dynamic programming, and how does each method approach the problem of finding an optimal policy?
Value iteration and policy iteration are two fundamental algorithms in dynamic programming used to solve Markov Decision Processes (MDPs) in the context of reinforcement learning. Both methods aim to determine an optimal policy that maximizes the expected cumulative reward for an agent navigating through a stochastic environment. Despite their shared objective, they differ significantly in
What are the key components of a Markov Decision Process (MDP) and how do they contribute to defining the environment in reinforcement learning?
A Markov Decision Process (MDP) is a mathematical framework used to model decision-making problems where outcomes are partly random and partly under the control of a decision-maker. It is a cornerstone concept in the field of reinforcement learning and dynamic programming. The key components of an MDP are states, actions, transition probabilities, rewards, and a