Intro

The approach we explore, called reinforcement learning, is much more focused on goal-directed learning from interaction than are other approaches to machine learning.

Reinforcement learning is learning what to do—how to map situations to actions—so as to maximize a numerical reward signal.

RL v/s Supervised

Reinforcement learning differs fundamentally from supervised learning. While supervised learning dominates current machine learning research, it relies on:

• Learning from labeled training examples
• External supervision providing situation-label pairs
• Direct mapping between situations and their correct labels

RL v/s Unsupervised

Reinforcement learning differs from unsupervised learning in several key ways:

• Unsupervised learning focuses on finding hidden patterns in unlabeled data
• While supervised and unsupervised learning may seem to cover all machine learning approaches, they don't
• Though reinforcement learning doesn't use labeled examples like supervised learning, it's distinct because:
  • It aims to maximize a reward signal
  • It's not focused on discovering hidden data structures

Tradeoff

One of the key challenges unique to reinforcement learning is the exploration-exploitation trade-off:

• Exploitation
  • Agent must use actions proven effective in the past
  • Focuses on maximizing immediate rewards
• Exploration
  • Agent needs to try new, untested actions
  • Required to discover potentially better strategies

This creates a fundamental dilemma: exclusive focus on either strategy leads to failure. The agent must balance both by:

• Testing various actions
• Gradually favoring the most effective ones
• Repeatedly testing actions in stochastic environments

Despite decades of mathematical research, this exploration-exploitation dilemma remains an open challenge in the field.

Agent type approach