How is Model-Free RL Different from Model-Based RL?
1. Learning Process:
- Model-Free RL: Learns policies or value functions directly from observed transitions and rewards.
- Model-Based RL: Learns a model of the environment’s dynamics first and then uses this model to plan and simulate future actions.
2. Efficiency:
- Model-Free RL: Often requires more real-world interactions to learn an optimal policy.
- Model-Based RL: Can be more sample-efficient as it can simulate many interactions using the learned model.
3. Complexity:
- Model-Free RL: Generally simpler to implement since it does not require learning a model.
- Model-Based RL: More complex due to the need to learn and maintain an accurate model of the environment.
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.