Overview of Model-Free Reinforcement Learning
Model-free reinforcement learning refers to methods where the agent learns directly from interactions with the environment without a model of the environment’s dynamics. The agent learns policies or value functions based solely on observed rewards and state transitions. There are two main categories within model-free RL:
- Policy-based methods: These methods directly optimize the policy that maps states to actions. Examples include REINFORCE and Proximal Policy Optimization (PPO).
- Value-based methods: These methods learn the value of taking certain actions in certain states. Examples include Q-Learning and Deep Q-Networks (DQN).
Characteristics:
- No explicit model: The agent does not construct or use a model of the environment.
- Direct learning: The agent learns policies or value functions directly from experiences.
- Examples: Q-Learning, SARSA, REINFORCE, and PPO.
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.