Model-based and model-free reinforcement learning are two approaches to solving reinforcement learning (RL) problems, and they differ in how they represent and utilize knowledge about the environment's dynamics.
Model-Based Reinforcement Learning :
* In model-based RL, the agent learns an explicit model of the environment's dynamics, which includes the transition probabilities P(s'|s, a) and the expected rewards R(s,a).
* Once the agent has learned the model, it can use planning algorithms, such as dynamic programming or Monte Carlo simulation, to simulate future trajectories and evaluate different action sequences without interacting with the real environment.
* By utilizing the learned model, model-based RL can potentially make more informed decisions and require fewer interactions with the environment to learn optimal policies.
* However, model-based RL relies heavily on the accuracy of the learned model, and inaccuracies or complexity in the model can lead to suboptimal or unstable performance.
Model-Free Reinforcement Learning :
* In model-free RL, the agent does not explicitly learn a model of the environment's dynamics. Instead, it learns a policy or action-value function directly from interaction with the environment.
* Model-free RL algorithms, such as Q-learning and SARSA, learn to estimate the value of actions or policies based on observed rewards and state transitions without explicitly modeling the transition probabilities.
* Model-free RL is often more flexible and can handle complex environments where the dynamics are unknown or difficult to model accurately.
* However, model-free RL may require more interactions with the environment to learn effective policies, especially in environments with sparse rewards or complex dynamics.
