Model-free method
A model-free method is a reinforcement-learning approach that learns policies or value functions directly from experience without an explicit model of environment dynamics and rewards.
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.
- How it is used
- Frames reinforcement-learning decisions as learned action preferences rather than predictions from an explicit environment model.
- 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 you can do after
- Apply via Q-learning by training a Q-table from simulated actions, rewards, and changing states.
Read how Grokking AI Algorithms, Second Edition explains model-free method →
Optimization Algorithms: AI techniques for design, planning, and control problems
Model-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.
- How it is used
- Frames direct policy or value learning from experience as an alternative to constructing an environment dynamics model.
- How deep it goes
- Defines model-free learning, contrasts it with model-based learning, and distinguishes on-policy from off-policy methods without deriving algorithms.
- Examples
- Uses A2C, PPO, routing, ride-sharing dispatch, freight delivery, taxi dispatch, and traffic signal control as examples.
- What you can do after
- Apply it to optimization and control problems where policies can learn directly from interaction experiences and rewards.
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.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.