What is model-based reinforcement learning?

Theoretical Background:

• Model-Based Reinforcement Learning: At its core, model-based RL involves creating an explicit model of the environment's dynamics. This model predicts the next state and reward given a current state and action. Planning can then be done using this model, allowing for strategies such as trajectory optimization or model predictive control.

• Model-Free Reinforcement Learning: This approach skips the modeling step and focuses on learning directly from the experiences obtained by interacting with the environment. Techniques such as Q-learning, SARSA, and policy gradient methods fall under this category.

Practical Applications:

• Model-Based RL: Useful in scenarios with expensive or limited interaction capabilities, like robotics (where interactions are costly) or personalized medicine (where patient data is limited).

• Model-Free RL: Commonly applied in domains where interactions are cheap and frequent, such as training agents in video games or financial trading simulations.

Code Example:

Consider a simple environment modeled as a Markov Decision Process (MDP). In Python, a model-based approach might involve learning a transition model using a neural network, while a model-free approach might directly learn a Q-function.

Trade-offs:

• Sample Efficiency: Model-based methods can be more sample-efficient because they leverage the learned model to generate additional training data, reducing the need for real interactions. This is particularly beneficial in environments where obtaining new samples is costly or time-consuming.

• Robustness: Model-free methods do not rely on an explicit model and thus are not affected by potential inaccuracies in model predictions, making them potentially more robust in environments with complex or unpredictable dynamics.

Diagram:

graph TD;
    A[Real Environment] -->|Interact| B[Model-Free RL];
    B --> F[Policy/Value Function];
    A -->|Learn Dynamics| C[Model-Based RL];
    C --> D[Model of Environment];
    D --> E[Planning/Simulation];
    E --> F

References:

• Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. This book covers foundational concepts in both model-based and model-free RL.
• Silver, D., & Huang, A. (2016). Mastering the game of Go with deep neural networks and tree search. Nature. This paper discusses the use of model-based techniques in combination with deep learning.