Overview of Model-Based Reinforcement Learning

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:...

Overview of Model-Based Reinforcement Learning

Model-based reinforcement learning involves building a model of the environment’s dynamics. The agent uses this model to simulate experiences and make decisions. There are two primary components:...

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....

How is Model-Free RL Different from Model-Based RL?

1. Learning Process:...

How is Model-Based Reinforcement Learning Different from Model-Free RL?

1. Utilization of the Environment:...

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...

Scenario: Autonomous Navigation in a Complex Environment

Imagine a scenario where an autonomous drone is tasked with navigating through a complex and dynamic environment, such as a forest, to deliver medical supplies to a remote location. The environment is filled with obstacles like trees, branches, and varying terrain, making it crucial for the drone to plan its path efficiently and adapt quickly to any changes....

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....