How is Model-Based Reinforcement Learning Different from Model-Free RL?
1. Utilization of the Environment:
- Model-Based RL: Actively builds and refines a model of the environment to predict outcomes and plan actions.
- Model-Free RL: Does not use an internal model and relies on direct experience and trial-and-error.
2. Adaptability:
- Model-Based RL: Can adapt more quickly to changes in the environment if the model is accurate.
- Model-Free RL: May take longer to adapt as it relies on accumulated experience.
3. Computational Requirements:
- Model-Based RL: Typically requires more computational resources due to the complexity of model learning and planning.
- Model-Free RL: Often less computationally intensive, focusing on direct learning from experience.
Differences between Model-free and Model-based Reinforcement Learning
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward. Two primary approaches in RL are model-free and model-based reinforcement learning. This article explores the distinctions between these two methodologies.