methodology

Model-Based Reinforcement Learning

Model-Based Reinforcement Learning (MBRL) is a machine learning approach where an agent learns a model of the environment's dynamics (e.g., transition probabilities and rewards) and uses this model to plan optimal actions, rather than directly learning a policy from experience. It involves two main components: learning a predictive model from data and using this model for planning or policy optimization. This contrasts with model-free methods that rely on trial-and-error without an explicit environment model.

Also known as: MBRL, Model-Based RL, Model Based Reinforcement Learning, Model-Based Learning, Dyna
🧊Why learn Model-Based Reinforcement Learning?

Developers should learn MBRL when working on applications where sample efficiency is critical, such as robotics, autonomous systems, or real-world tasks where data collection is expensive or risky, as it can reduce the number of interactions needed with the environment. It is also useful in scenarios where the environment is partially observable or complex, allowing for better generalization and planning through simulated rollouts. However, it requires accurate model learning and can be computationally intensive during planning phases.

Compare Model-Based Reinforcement Learning

Model-Based Reinforcement Learning vs Model Free Reinforcement Learning→Model-Based Reinforcement Learning vs Imitation Learning→Model-Based Reinforcement Learning vs Evolutionary Algorithms→

Learning Resources

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Model-Based Reinforcement Learning: A Survey
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DeepMind's Introduction to Model-Based RL
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Coursera: Reinforcement Learning Specialization
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YouTube: Model-Based RL Explained
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Book: Reinforcement Learning: An Introduction
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Related Tools

Reinforcement LearningMachine LearningDeep LearningPlanning AlgorithmsSimulation

Alternatives to Model-Based Reinforcement Learning

Model Free Reinforcement LearningImitation LearningEvolutionary Algorithms