Similarities Between Model-Free and Model-Based Reinforcement Learning
- Goal: Both approaches aim to learn an optimal policy that maximizes cumulative rewards.
- Interaction: Both require interaction with the environment to gather data.
- Learning: Both involve learning from experiences, though the methods of utilizing these experiences differ.
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.