Reference 4 stops to get here

AlphaGo

DeepMind's Go-playing AI that defeated world champions using deep RL and tree search.

Your route here

4 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. Exploration vs Exploitation ✓ understood

    The RL dilemma of trying new actions (exploration) versus using known good actions (exploitation) to maximize reward.

  4. Monte Carlo Tree Search ✓ understood

    A search algorithm combining tree search with random sampling, used in game-playing AIs.

  5. AlphaGo · you are here ✓ understood

DeepMind’s Go-playing AI that defeated world champions using deep RL and tree search.

This concept is essential for understanding emerging & advanced and forms a key part of modern AI systems.

  • Reinforcement Learning
  • Game Playing
  • MCTS

Where it sits

Before this

Reinforcement Learning Monte Carlo Tree Search
AlphaGo

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Agents & RL Deep Q-Network Combining Q-learning with deep neural networks to handle high-dimensional state spaces, enabling RL for complex tasks like Atari games. Agents & RL Policy Gradient RL methods that directly optimize the policy by computing gradients of expected reward with respect to policy parameters. Agents & RL Value Function A function estimating expected cumulative reward from a state (state-value) or state-action pair (action-value/Q-value). Foundations AlphaFold DeepMind's breakthrough AI system for predicting protein structures with near-experimental accuracy.