Model-free method

Q: How deeply does Optimization Algorithms: AI techniques for design, planning, and control problems cover model-free method?

Defines model-free learning, contrasts it with model-based learning, and distinguishes on-policy from off-policy methods without deriving algorithms.

As explained in 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.

A model-free method learns a policy or value function directly from interaction experience, without explicitly constructing the environment’s transition dynamics. It selects actions from observed histories and received rewards, improving through trial and error rather than planning with a learned environment model. section 12.1.3

Use these distinctions to position a model-free method:

Treat the environment model as unnecessary for learning; rewards may be unknown or only partially known. section 12.1.3
Separate on-policy and off-policy methods by the experience used: the current policy generates on-policy data, while another policy generates off-policy data. section 12.1.3
Apply the approach to routing, ride-sharing dispatch, freight delivery, taxi dispatch, and traffic signal control. section 12.2

Table 12.1 Model-based RL (MBRL) versus model-free RL (MFRL) (view table figure)

Aspects	Model-based RL (MBRL)	Model-free RL (MFRL)
Environment model	Uses a known model or learns a model of the environment (i.e., transition probabilities)	Skips models and directly learns what action to take when (without necessarily finding out the exact model of the action)
Rewards	Typically known or learned	Unknown or partially known. Model-free RL learns directly from the rewards received during interaction with the environment.
Actions	Selected to maximize the expected cumulative reward using the model	Selected to maximize the expected cumulative rewards based on the history of experiences
Policy	Policy learning is accomplished by learning a model of the environment dynamics.	Policy learning is achieved through trial and error, directly optimizing the policy based on observed experiences.
Design and tuning	MBRL can have a higher initial design and tuning effort due to model complexity. However, advancements are simplifying this process.	Requires less initial effort. However, MFRL hyperparameter tuning can also be challenging, especially for complex tasks.
Examples	AlphaZero, world models, and imagination-augmented agents (I2A)	Q-learning, advantage actor-critic (A2C), asynchronous advantage actor-critic (A3C), and proximal policy optimization (PPO)

Compare model-free and model-based reinforcement learning by model use, rewards, action selection, and policy learning. section 12.1.3

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

Cover of Optimization Algorithms: AI techniques for design, planning, and control problems

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Grokking AI Algorithms, Second EditionModel-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.

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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.

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