Model-free and model-based reinforcement learning (RL) differ primarily in whether they explicitly learn or use a model of the environment. In model-free RL, the agent learns a policy or value function directly from interactions with the environment, without building an internal representation of how the environment works. In contrast, model-based RL involves creating a predictive model of the environment’s dynamics (e.g., how states transition and rewards are generated) and using that model to plan or improve decision-making. The choice between the two approaches hinges on trade-offs like sample efficiency, computational complexity, and the difficulty of modeling the environment accurately.
Model-free methods, such as Q-learning or policy gradient algorithms, focus on learning through trial and error. For example, in Q-learning, the agent updates a table (or neural network) that estimates the expected reward for taking an action in a state, using actual experiences (state, action, reward, next state) to refine these estimates. These approaches avoid the need to understand the environment’s mechanics, making them simpler to implement in cases where the environment is complex or stochastic. However, they often require large amounts of interaction data to converge, which can be impractical in real-world scenarios like robotics, where collecting data is slow or costly. Model-free algorithms are widely used in settings like game playing (e.g., training agents for Atari games) where simulations are fast and abundant.
Model-based approaches, such as Dyna-Q or Monte Carlo Tree Search (used in AlphaGo), explicitly learn or assume a model of the environment. For instance, Dyna-Q combines real experiences with simulated rollouts from a learned model to update its policy more efficiently. By simulating potential future states, model-based agents can plan ahead and make decisions with fewer actual interactions. However, building an accurate model is challenging, especially in environments with high dimensionality or partial observability. If the model is flawed—for example, if it underestimates the randomness of state transitions—the agent’s planning might lead to poor decisions. Model-based methods are advantageous in domains like autonomous driving or industrial control, where safety and sample efficiency are critical, but they require careful design to ensure the model remains reliable.