Action-value function

As explained in Build a Robo-Advisor with Python (From Scratch)

A Q-function maps a state and an action to the estimated cumulative reward obtained by taking that action and following a policy afterward.

How Build a Robo-Advisor with Python (From Scratch) covers it

How deep it goes

Explains the update equation and implementation loop, stopping short of broader algorithmic variations.

Examples

Uses wealth-time-action arrays, random wealth transitions, terminal goals, exploratory action selection, and repeated Python episodes.

What it assumes

Assumes state, action, policy, reward, and value-function ideas while contrasting state-only values with state-action values.

What you can do after

Builds a Q-learning loop that estimates action values and supports policy selection through exploration and exploitation.

How it connects

Connects reinforcement learning, policies, value functions, dynamic programming, asset allocation, learning rates, and discount rates.

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Also explained in

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  Grokking AI Algorithms, Second EditionA Q-function maps a state-action pair to the expected cumulative reward obtained by taking the action and subsequently following a policy.
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  Optimization Algorithms: AI techniques for design, planning, and control problemsAn action-value function estimates the expected cumulative future reward from taking a specified action in a state and then following a policy.
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  Evolutionary Deep Learning: Genetic algorithms and neural networksA Q-function maps a state and action to an estimate of the expected cumulative reward from taking that action and following a policy thereafter.
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  liveBookmanning.com
  Reinforcement Learning from Human FeedbackA Q-function maps a state-action pair to a scalar estimate of expected future reward.

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Related concepts

• Q-learningused by · Q-learning is a model-free reinforcement-learning algorithm that estimates state-action values through updates based on rewards and subsequent state values.
• value functionkind of · A value function assigns or estimates the expected cumulative reward, utility, or quality associated with a state or action under a policy or decision rule.
• Bellman equationkind of · A Bellman equation recursively relates a state or action value to an immediate reward and the expected discounted value of successor states or actions.
• Q-tablepart of · A Q-table is a lookup table storing estimated action values for state-action pairs in a tabular reinforcement-learning system.
• advantage functionused by · An advantage function in reinforcement learning estimates how much better or worse an action’s expected return is than a baseline value for the corresponding state or policy.

This explainer was generated by Manning's knowledge graph from the text of Build a Robo-Advisor with Python (From Scratch). The section links point to the parts of the book it draws on.