Model-based Reinforcement Learning

Model-based Reinforcement Learning refers to learning optimal behavior indirectly by learning a model of the environment by taking actions and observing the outcomes that include the next state and the immediate reward. The models predict the outcomes of actions and are used in lieu of or in addition to interaction with the environment to learn optimal policies.

References

Model-Based Reinforcement Learning. In Encyclopedia of Machine Learning, pp. 642-644. Springer, 2010. Link.