Reinforcement Learning

What Reinforcement Learning is

Reinforcement learning frames a problem as an agent, states, actions and rewards. The agent must discover which sequences of actions lead to long-term reward, balancing exploration of new strategies with exploitation of what already works.

How it works

Value-based methods estimate the expected return of state-action pairs; policy-gradient methods adjust the action distribution directly. Training runs millions of episodes in simulation, and reward shaping is usually the hardest part of the engineering.

Why it matters

RL underpins game-playing breakthroughs, robotic control and — through RLHF — the alignment step that turns a raw language model into a usable assistant.

Common uses

• →Robotic manipulation and locomotion
• →Data-centre cooling and energy optimisation
• →Recommendation with long-term engagement objectives
• →Preference tuning of language models