What are model-free and model-based reinforcement learning methods?

Model-free and model-based reinforcement learning (RL) are two broad approaches for training agents to make decisions in environments. The key difference lies in whether the agent uses an explicit model of the environment. Model-free methods learn policies or value functions directly from interactions with the environment, without building a representation of how the environment works. Model-based methods, in contrast, first learn or assume a model of the environment (e.g., predicting state transitions or rewards) and use that model to plan or simulate outcomes before acting. Model-free approaches are often simpler but require more data, while model-based methods aim to improve efficiency by leveraging the learned model.

Model-free RL focuses on trial-and-error learning. Algorithms like Q-Learning, Deep Q-Networks (DQN), and Policy Gradient methods (e.g., REINFORCE) fall into this category. For example, in a game-playing scenario, a model-free agent might learn to associate specific game states with high-value actions by repeatedly playing and observing rewards, without understanding the rules governing state transitions. These methods are widely used because they avoid the complexity of modeling the environment. However, they can be sample-inefficient: training a robot to walk via pure model-free RL might require millions of simulated steps, as the agent must explore every possible action in various states to discover optimal behavior.

Model-based RL incorporates a learned or predefined model of the environment to simulate outcomes and plan ahead. For instance, Dyna-Q combines model-free Q-Learning with a learned transition model to generate synthetic experiences for training. Monte Carlo Tree Search (MCTS), used in systems like AlphaGo, simulates future game states to evaluate moves without relying solely on past experience. Model-based methods can achieve higher sample efficiency because the agent can “imagine” outcomes without always interacting with the real environment. However, their performance depends heavily on the accuracy of the model. If the model is flawed—for example, a robot’s physics simulator mispredicts friction—the agent’s plans may fail in the real world. Developers often choose model-based approaches when environment interactions are costly (e.g., robotics or healthcare) but must balance this with the risk of model bias.