Model-based reinforcement learning (RL) algorithms learn an explicit model of the environment’s dynamics to plan or optimize policies. Unlike model-free methods, which directly learn policies or value functions from interactions, model-based approaches first build a predictive model of how the environment responds to actions. Common examples include Dyna, Model-Based Policy Optimization (MBPO), Dreamer, and Probabilistic Inference for Learning Control (PILCO). These algorithms often prioritize sample efficiency by using the learned model to simulate experiences, reducing the need for costly real-world interactions.
One prominent algorithm is Dyna, which combines real-world data with simulated rollouts from a learned model. For instance, Dyna-Q alternates between updating a Q-table using actual experiences and generating synthetic transitions from the model to refine the policy. Another example is PILCO, designed for continuous control tasks. PILCO uses Gaussian processes to model dynamics and leverages probabilistic inference to optimize policies, making it effective in low-data settings. Modern approaches like MBPO extend these ideas by training an ensemble of neural network models to reduce prediction errors. The policy is then optimized using short simulated trajectories, balancing exploration and exploitation. Dreamer, a more recent algorithm, learns a latent dynamics model from pixels and uses it to train policies entirely in imagination, enabling efficient learning from high-dimensional observations.
The primary advantage of model-based RL is reduced reliance on real-world interactions, which is critical in domains like robotics where data collection is slow or expensive. However, model inaccuracies can lead to suboptimal policies if the simulated data diverges from reality. Techniques like ensemble models (as in MBPO) or uncertainty-aware planning (as in PILCO) help mitigate this. Applications range from game-playing agents, like AlphaZero’s use of a learned model for Monte Carlo tree search, to industrial robotics for precise control. While model-based methods require careful tuning of the model-learning process, their efficiency and scalability make them a practical choice for many real-world problems.