Standard 8 stops to get here

Deep Q-Network

Combining Q-learning with deep neural networks to handle high-dimensional state spaces, enabling RL for complex tasks like Atari games.

Your route here

8 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. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  3. Reinforcement Learning ✓ understood

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

  4. Reward ✓ understood

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

  5. Agent ✓ understood

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

  6. Policy ✓ understood

    A strategy or mapping from states to actions that defines the agent's behavior in reinforcement learning.

  7. Value Function ✓ understood

    A function estimating expected cumulative reward from a state (state-value) or state-action pair (action-value/Q-value).

  8. Q-Learning ✓ understood

    A model-free RL algorithm that learns action-value functions (Q-values) to determine optimal actions in each state.

  9. Deep Q-Network · you are here ✓ understood

Where it sits

Before this

Q-Learning Neural Network
Deep Q-Network

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Agents & RL Policy Gradient RL methods that directly optimize the policy by computing gradients of expected reward with respect to policy parameters. Agents & RL Actor-Critic RL architecture with two components: an actor (policy) that selects actions and a critic (value function) that evaluates them. Agents & RL Model-Free RL Reinforcement learning directly learning policies or value functions without modeling environment dynamics. Agents & RL AlphaGo DeepMind's Go-playing AI that defeated world champions using deep RL and tree search. Agents & RL Value Function A function estimating expected cumulative reward from a state (state-value) or state-action pair (action-value/Q-value).

In the research

All papers →

A paper that builds on Deep Q-Network .