Markov Decision Process
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making problems with probabilistic outcomes. MDPs are the formal foundation for reinforcement learning algorithms.
Understanding Markov Decision Process
A Markov decision process (MDP) is a mathematical framework for modeling sequential decision-making under uncertainty, defining states, actions, transition probabilities, and rewards. MDPs formalize the problem that reinforcement learning agents solve: finding an optimal policy that maximizes cumulative reward over time. At each step, the agent observes the current state, takes an action, receives a reward, and transitions to a new state according to probabilistic dynamics. The Markov property ensures that the next state depends only on the current state and action, not the full history. Dynamic programming algorithms like value iteration and policy iteration can solve small MDPs exactly, while larger problems require approximate methods such as deep reinforcement learning. MDPs underpin applications from robotics and game AI to resource allocation and autonomous systems, providing the theoretical foundation for the minimax algorithm and other planning approaches.
Category
Reinforcement Learning
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Deep Reinforcement Learning
Deep reinforcement learning combines deep neural networks with reinforcement learning algorithms to handle complex, high-dimensional environments. It has achieved superhuman performance in games like Go, chess, and Atari.
Exploration vs Exploitation
Exploration vs exploitation is a fundamental dilemma in reinforcement learning between trying new actions to discover better rewards versus leveraging known good actions. Balancing both is key to optimal long-term performance.
Imitation Learning
Imitation learning is a technique where an AI agent learns to perform tasks by observing and mimicking expert demonstrations. It bridges the gap between supervised learning and reinforcement learning.
Inverse Reinforcement Learning
Inverse reinforcement learning infers the reward function that an expert is optimizing by observing their behavior. It enables AI systems to learn goals and preferences from demonstrations.
Minimax
Minimax is a decision-making algorithm used in adversarial settings where one player tries to maximize their score while the other minimizes it. It is the classical approach for game-playing AI systems.
Policy
A policy in reinforcement learning is a function that maps states to actions, defining the agent's behavior strategy. The goal of RL is to learn an optimal policy that maximizes cumulative reward.
Q-Learning
Q-learning is a model-free reinforcement learning algorithm that learns the value of actions in states to find an optimal policy. It uses a Q-table or neural network to estimate expected cumulative rewards for each state-action pair.
Reinforcement Learning
Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by receiving rewards or penalties for its actions in an environment. It has achieved breakthroughs in game playing, robotics, and AI alignment.