Key Differences in between Model-free and Model-based Reinforcement Learning
Feature | Model-Free RL | Model-Based RL |
---|---|---|
Learning Approach | Direct learning from environment | Indirect learning through model building |
Efficiency | Requires more real-world interactions | More sample-efficient |
Complexity | Simpler implementation | More complex due to model learning |
Environment Utilization | No internal model | Builds and uses a model |
Adaptability | Slower to adapt to changes | Faster adaptation with accurate model |
Computational Requirements | Less intensive | More computational resources needed |
Examples | Q-Learning, SARSA, DQN, PPO | Dyna-Q, Model-Based Value Iteration |
Understanding these differences can help practitioners choose the appropriate method for their specific RL problem, balancing the trade-offs between simplicity, efficiency, and computational demands.
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.