Standard 5 stops to get here

Markov Decision Process

A mathematical framework for modeling sequential decision-making with states, actions, rewards, and transition probabilities.

Your route here

5 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Reinforcement Learning ✓ understood

    Learning through interaction with an environment, receiving rewards or penalties to learn optimal behavior policies.

  3. Reward ✓ understood

    A scalar feedback signal indicating how good an action was, used to train reinforcement learning agents.

  4. Agent ✓ understood

    In RL, the learner or decision-maker that takes actions in an environment to maximize cumulative reward.

  5. Environment ✓ understood

    In RL, the world the agent interacts with, providing states, accepting actions, and returning rewards.

  6. Markov Decision Process · you are here ✓ understood

Where it sits

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Environment Reward
Markov Decision Process

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Agents & RL Policy A strategy or mapping from states to actions that defines the agent's behavior in reinforcement learning. Agents & RL Value Function A function estimating expected cumulative reward from a state (state-value) or state-action pair (action-value/Q-value). Agents & RL Q-Learning A model-free RL algorithm that learns action-value functions (Q-values) to determine optimal actions in each state. Foundations Hidden Markov Model A statistical model with hidden states that transition probabilistically, generating observable outputs. Agents & RL Reinforcement Learning Learning through interaction with an environment, receiving rewards or penalties to learn optimal behavior policies.

In the research

All papers →

3 papers that build on Markov Decision Process .