Reference 3 stops to get here

Multi-Agent RL

Reinforcement learning with multiple agents that interact and potentially cooperate or compete.

Your route here

3 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. Agent ✓ understood

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

  4. Multi-Agent RL · you are here ✓ understood

Reinforcement learning with multiple agents that interact and potentially cooperate or compete.

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

  • Reinforcement Learning
  • Game Theory
  • Cooperation

Where it sits

Before this

Reinforcement Learning Agent
Multi-Agent RL

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Agents & RL Hierarchical RL Learning policies at multiple levels of abstraction, with high-level goals and low-level skills. Agents & RL Exploration vs Exploitation The RL dilemma of trying new actions (exploration) versus using known good actions (exploitation) to maximize reward. Agents & RL AlphaGo DeepMind's Go-playing AI that defeated world champions using deep RL and tree search. Agents & RL Environment In RL, the world the agent interacts with, providing states, accepting actions, and returning rewards.