How does the concept of the Markov property simplify the modeling of state transitions in MDPs, and why is it significant for reinforcement learning algorithms?
Tuesday, 11 June 2024
by EITCA Academy
The Markov property is a fundamental concept in the study of Markov Decision Processes (MDPs) and plays a crucial role in simplifying the modeling of state transitions. This property asserts that the future state of a process depends only on the present state and action, not on the sequence of events that preceded it. Mathematically,
What are the key components of a Markov Decision Process (MDP) and how do they contribute to defining the environment in reinforcement learning?
Tuesday, 11 June 2024
by EITCA Academy
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