Reinforcement Learning

Learning to act by maximizing cumulative reward through interaction with an environment.

Reinforcement Learning is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart LR
  A([Agent]) -- action --> E([Environment])
  E -- state + reward --> A

Key topics

• Markov decision processes

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  The formal framework: states, actions, rewards, transitions, and policies.

• Value & policy methods

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  Q-learning, policy gradients, actor-critic, and when to use each.

• Deep RL

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  Combining RL with deep networks (DQN, PPO) for high-dimensional problems.

• RLHF

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  Reinforcement learning from human feedback — how LLMs are aligned to preferences.

• Multi-agent RL

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  Many agents learning together — cooperation, competition, and emergent behaviour.

Related areas

Machine Learning · AI Agents & Autonomy · Robotics & Embodied AI

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