While model-free reinforcement learning methods learn policies or value functions directly from interactions, model-based RL takes a different path. As introduced, the core idea is to first build an internal model of the environment. This typically involves learning two components from experience:

Transition Dynamics Model: An approximation of the state transition function, often represented as $P(s'|s, a)$ , which predicts the probability of transitioning to the next state $s'$ given the current state $s$ and action $a$ .
Reward Model: An approximation of the reward function, such as $R(s, a)$ or $R(s, a, s')$ , which predicts the expected immediate reward.

But why go through the effort of building this intermediate model instead of learning the policy or value function directly? There are several compelling reasons, primarily centered around efficiency and planning capabilities.

Improved Sample Efficiency

Perhaps the most significant motivation for using model-based RL is the potential for drastically improved sample efficiency. Interacting with the real environment can be costly, time-consuming, or even dangerous. Consider training a robot arm to assemble an object; each physical trial takes time and risks damage. Similarly, collecting data in domains like healthcare or finance can be expensive and slow.

Model-free algorithms often require a large number of environment interactions to converge because they learn solely from trial and error. If an agent can learn a reasonably accurate model of the environment dynamics, it can generate simulated experiences internally without needing further real-world interaction. The agent can effectively "imagine" taking actions in different states and observe the predicted outcomes according to its learned model.

This simulated data can then be used to update the agent's policy or value function through planning or by treating it as if it were real experience. Algorithms like Dyna-Q explicitly leverage this idea: they use real interactions to update the policy/value function and the model, then perform additional updates using simulated transitions drawn from the model. This ability to "replay" and learn from simulated trajectories allows the agent to extract more value from each real interaction, often leading to faster learning with less real-world data compared to purely model-free counterparts.

Comparison of typical interaction loops in model-free RL versus a model-based approach like Dyna-Q. The model-based loop includes explicit steps for learning an environment model and using it for planning or generating simulated experiences from that model.

Explicit Planning Capabilities

Having an explicit model unlocks the ability to perform planning. While model-free methods learn reactive policies ( $s \rightarrow a$ ) or value functions ( $s \rightarrow V(s)$ or $(s,a) \rightarrow Q(s,a)$ ), they lack a mechanism to explicitly reason about future sequences of actions beyond what's implicitly captured in the learned function.

With a model, an agent can perform lookahead search. It can simulate multiple possible sequences of actions starting from the current state, predict the resulting states and rewards using its learned model, and evaluate the long-term outcomes of these sequences. This allows the agent to choose actions based on anticipating their future consequences. Techniques that heavily rely on this include:

Trajectory Sampling: Simulating full trajectories under different action sequences and choosing the best starting action.
Monte Carlo Tree Search (MCTS): A powerful heuristic search algorithm that balances exploration and exploitation within the simulated future, famously used in game-playing agents like AlphaGo/AlphaZero.

This planning capability can be particularly advantageous in problems where careful deliberation and anticipating opponent moves (in multi-agent settings) or complex environmental dynamics is beneficial.

Potential for Better Generalization and Adaptation

Learning the underlying dynamics of an environment might lead to representations that generalize better than purely model-free approaches in some scenarios. For instance, if the core transition dynamics $P(s'|s, a)$ remain constant but the reward function $R(s, a, s')$ changes (e.g., the goal location moves), an agent with a learned model might adapt more quickly. It already "understands" how the world works; it just needs to re-plan using the existing model with the new reward information. A model-free agent would likely need substantial new interaction data to relearn its policy or value function from scratch or near-scratch. However, this benefit is highly dependent on the accuracy of the learned model and the nature of the change.

Why Consider Model-Based RL Now?

Model-free deep RL algorithms like DQN and PPO have achieved remarkable success. However, their often-significant sample complexity remains a practical bottleneck. Model-based methods offer a complementary set of tools. By explicitly modeling the environment, they aim to make more efficient use of collected data and enable sophisticated planning, potentially leading to faster learning and better performance in certain types of problems, especially those where interactions are expensive or lookahead reasoning is advantageous.

Of course, model-based RL is not without its own challenges. The performance heavily relies on the accuracy of the learned model. If the model is poor (high model bias), planning based on it can lead to suboptimal or even catastrophic policies. Furthermore, learning an accurate model, especially for complex, high-dimensional environments, can be difficult, and the planning process itself can be computationally intensive. We will delve into these challenges and the techniques developed to mitigate them in the subsequent sections of this chapter.