Mastering Reinforcement Learning: Understanding Markov States, Transition Probabilities, and Decision-Making Processes
In the realm of machine learning, Reinforcement Learning (RL) stands out as a unique approach where an agent learns to interact with its environment to maximize a reward. This article provides a foundation for getting started with reinforcement learning, focusing on the Markov Decision Process (MDP).
MDP is an extension of the Markov Chain, a stochastic process where what happens now depends on the current state, not what happened in the past. In an MDP, the agent's environment is defined by specifying states, actions, state transition probabilities, and reward functions, adhering to the Markov property that the future depends only on the current state and action.
The goal in an MDP is to find a policy that optimizes the agent’s decisions at every time step to maximize the overall reward. This policy, a mapping from state space S to action space A, prescribes the best action to take in each state to achieve the highest long-term reward. Common methods to find this optimal policy include algorithms like value iteration, policy iteration, and Q-learning.
A Markov state has a property that all future states depend on the current state only, making the learning process more efficient and the agent's behavior more predictable. This property is particularly useful when modelling complex systems, such as a self-driving car. In this context, states could represent different positions and velocities, actions could represent different actions, and rewards could represent the value or utility of actions, such as avoiding collisions and arriving at the destination quickly.
It's important to note that RL differs from supervised learning and unsupervised learning, which use labeled examples and unlabeled data, respectively. RL learns from the consequences of its actions, making it particularly useful for a wide range of applications, including robotics, natural language processing, and gaming.
For those interested in delving deeper into reinforcement learning, the next article will discuss concepts such as value function, dynamic programming, solving a Markov decision process, and partial observability MDP. This series aims to provide a comprehensive understanding of reinforcement learning, drawing from resources like "Dynamic Programming and Markov Processes" by Ronald A. Howard and "Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions" by Warren B. Powell.
Artificial-intelligence, incorporated into reinforcement learning, learns from the consequences of its actions, making it indispensable in various real-world applications such as robotics, natural language processing, and gaming. In an artificial-intelligence-driven MDP, the agent's behavior becomes more predictable due to the Markov property, which enables efficient learning and facilitates the modeling of complex systems like a self-driving car.
