Markov Decision Processes (MDPs) are a mathematical framework used in reinforcement learning to model sequential decision-making under uncertainty. MDPs provide a formal way of representing an environment in which an agent interacts to maximize long-term rewards. Let's explore the key elements of MDPs:
States: MDPs consist of a set of states representing the different configurations of the environment. Each state represents a specific situation in which the agent can find itself.
Actions: At each state, the agent can choose from a set of available actions. Actions represent the different decisions or behaviors the agent can take.
Transition Probability: MDPs define the probabilities of transitioning from one state to another after taking a specific action. These probabilities capture the dynamics of the environment, determining the next state the agent will end up in.
Rewards: After each action, the agent receives a reward that provides feedback on the quality of the action taken. The goal in reinforcement learning is to maximize the accumulated rewards over time.
By modeling the environment as an MDP, we can use various algorithms to find an optimal policy that guides the agent's decision-making process. These algorithms aim to maximize the expected cumulative reward by learning the optimal action to take at each state. Common approaches include value iteration and policy iteration.
So, when dealing with dynamic decision-making problems under uncertainty, MDPs offer a powerful framework to assist reinforcement learning agents in making intelligent choices. Understanding MDPs can greatly enhance your grasp of reinforcement learning concepts and algorithms!