Scenario: Learning to Play a Novel Video Game
Consider a scenario where an artificial intelligence (AI) agent is learning to play a new, highly complex video game that has just been released. The game involves a vast, open-world environment with numerous interactive elements, characters, and intricate gameplay mechanics. The game world is detailed and unpredictable, with events and interactions that cannot be easily modeled.
Why Model-Free RL is Suitable?
- Highly Complex and Unpredictable Environment:
- Unmodelable Dynamics: The game environment is too complex to be accurately modeled. It includes random events, hidden rules, and interactive elements that are difficult to predict.
- Rich, Diverse Experiences: The game offers a vast array of possible states and actions, making it impractical to build a comprehensive model.
- Direct Learning from Interactions:
- Trial-and-Error: The AI can learn effective strategies through direct interaction with the game, improving its performance based on the rewards received.
- Adaptation to Game Mechanics: The agent can adapt to the game mechanics and develop tactics through repeated gameplay, learning from successes and failures.
- Exploration of Unknown Strategies:
- Discovering Optimal Policies: Model-free RL allows the agent to explore and discover optimal policies by trying various actions and observing their outcomes.
- Learning from Rewards: The agent learns which actions lead to higher rewards, refining its strategy without needing an explicit model of the game’s dynamics.
Why Model-Based RL is Not Suitable?
- Infeasibility of Accurate Modeling:
- Complex Interactions: The game’s numerous interactions and hidden rules make it nearly impossible to create an accurate model. Model-based RL relies on having a precise model, which is unattainable in this scenario.
- Dynamic and Random Elements: The game’s random events and dynamic elements prevent the creation of a stable and reliable model.
- Resource and Time Constraints:
- Model Maintenance: Continuously updating and refining a model to reflect the game’s complexity would be computationally expensive and time-consuming.
- Simulation Limitation: Simulating the game’s intricate environment accurately would require immense computational power, making it impractical.
- Exploration Requirement:
- Initial Exploration Phase: Model-based methods require an extensive initial phase of exploration to build the model, which can be inefficient in a game with vast and unpredictable states.
- Immediate Adaptation: In a fast-paced game, immediate adaptation and learning from direct experiences are crucial, which model-free RL excels at.
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.