Reinforcement learning is a fascinating area of artificial intelligence research that has led to breakthroughs like DeepMind's AlphaGo programme defeating the world champion in the game of Go.

In reinforcement learning, an AI agent learns how to maximise rewards through trial-and-error interactions with its environment. Unlike supervised learning where the agent is given labelled training data, or unsupervised learning where the agent tries to find patterns in unlabelled data, reinforcement learning relies on the agent actively taking actions that affect the state of its environment in order to gather rewards. The agent must discover which actions yield the greatest rewards through exploration and exploitation.

The Agent-Environment Interaction Loop

At the core of reinforcement learning is the interaction between an agent and its environment. The agent selects actions and the environment responds by transitioning to a new state and giving the agent a reward. The agent must then choose a new action. This creates a loop: agent takes action -> environment transitions and gives reward -> agent takes another action -> etc. The agent's goal is to maximise cumulative reward over time by learning to select optimal actions.

For example, AlphaGo acts as the agent playing the game of Go against itself. Each valid move AlphaGo makes is an action that transitions the board to a new state. AlphaGo aims to get the highest final score against itself which represents the cumulative reward. By playing thousands of games through self-play, AlphaGo learned which sequence of moves brought the highest rewards.

Key Components of Reinforcement Learning

There are three key components of any reinforcement learning system:

• Policy: The agent's behaviour function that maps states to actions. It determines how the agent selects the next action given the current state of the environment. The policy is learned and improved through experience.

• Reward: A numeric feedback signal the agent receives after performing an action that transitions to a new state. The agent seeks to maximise cumulative reward over time. Rewards determine which states and actions are good or bad.

• Value function: Estimates the expected cumulative reward from any given state. Determines how "valuable" it is for the agent to be in a given state. Allows comparison of long-term rewards of different states.

The agent improves its policy using the rewards and values experienced during exploration to shift towards taking better actions over time.