World models in reinforcement learning (RL) are internal representations that allow an agent to simulate and predict the outcomes of its actions within an environment. Unlike model-free RL, where agents learn policies directly from interactions, model-based approaches (which use world models) focus on building a predictive understanding of how the environment works. A world model acts as a “simulator” within the agent, enabling it to forecast future states and rewards based on its current state and proposed actions. This reduces the need for constant real-world experimentation, making training more efficient. For example, in robotics, a world model could predict how a robot’s movements affect its position, allowing it to plan paths without physically testing every possible motion.
Implementing a world model typically involves training a neural network to approximate the environment’s dynamics. The model takes the current state and action as input and outputs the predicted next state and reward. Developers often use architectures like recurrent neural networks (RNNs) or transformers to capture temporal dependencies in sequential tasks. For instance, the Dreamer algorithm uses a latent dynamics model to compress high-dimensional observations (e.g., pixels from a camera) into a lower-dimensional latent space. This compressed representation allows the agent to perform long-horizon planning efficiently by simulating trajectories in the latent space. Another example is AlphaGo’s use of a policy network and tree search, which implicitly relies on a model of the game’s rules to evaluate future board states.
The benefits of world models include improved sample efficiency and the ability to perform “mental rehearsal” of actions before execution. However, their accuracy is critical: if the model’s predictions diverge from reality, the agent’s plans may fail. For example, a self-driving car relying on an imperfect world model might mispredict pedestrian behavior, leading to unsafe decisions. Balancing model complexity is also a challenge—overly simple models may lack predictive power, while overly complex ones become computationally expensive. Despite these trade-offs, world models remain a key tool in RL, particularly for tasks where real-world interactions are costly or time-consuming, such as industrial automation or climate modeling. Developers often combine them with model-free techniques to mitigate inaccuracies while retaining efficiency.