Model-free method

As explained in Grokking AI Algorithms, Second Edition

Model-free reinforcement learning is a reinforcement-learning approach in which an agent learns favorable actions through direct trial-and-error interactions without an explicit environment model.

A model-free method learns favorable actions through trial-and-error interactions with an environment, without first building an explicit model of how that environment works. section 10.9

Unlike model-based learning, which simulates possible outcomes using an internal representation, a model-free agent explores interactions and learns which actions work in different situations. section 10.9

A Q-learning implementation makes the method concrete:

• Represent each state as a row and each possible action as a column in a Q-table. section 10.6
• Store a quality score, or Q-value, for every state-action combination rather than storing only one chosen action. section 10.6
• Move through the environment, receive rewards, observe the next state, and update estimates from the outcome. section 10.6
• Balance exploration and exploitation by sometimes choosing a random action and otherwise selecting the action with the highest current score. section 10.6

Finding a route

Imagine an agent moving through a road or grid environment toward a goal. Its initial Q-values are guesses; after repeated moves and reward updates, the largest value in each state’s row points toward a favorable action. section 10.9 section 10.6

Figure 10.19 Examples of model-based and model-free reinforcement learning

Compare model-based route planning with model-free learning through a visual road-navigation example. section 10.9

How Grokking AI Algorithms, Second Edition covers it

How deep it goes

Explains trial-and-error learning through Q-tables, rewards, exploration, state changes, and action selection.

Examples

Uses road navigation and a grid-like simulator with states, movement commands, goals, rewards, and Q-table updates.

What it assumes

Assumes basic reinforcement-learning terms including states, actions, rewards, environments, and Q-learning.

What you can do after

Apply via Q-learning by training a Q-table from simulated actions, rewards, and changing states.

How it connects

Connects reinforcement learning, Q-learning, Q-tables, model-based learning, states, actions, and rewards.

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Also explained in

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  Optimization Algorithms: AI techniques for design, planning, and control problemsModel-free reinforcement learning is reinforcement learning in which an agent learns policies or value functions directly from experience without explicitly constructing an environment dynamics model.

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Related concepts

• Q-learningincludes · Q-learning is a model-free reinforcement-learning algorithm that estimates state-action values through updates based on rewards and subsequent state values.
• reinforcement learningkind of · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
• dynamic programmingalternative to · Dynamic programming is an algorithmic technique that solves problems by decomposing them into overlapping subproblems and storing or reusing their solutions.

This explainer was generated by Manning's knowledge graph from the text of Grokking AI Algorithms, Second Edition. The section links point to the parts of the book it draws on.