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.

Why Model-Based RL is Suitable?

1. Complex Environment Modeling:
   • Dynamic Obstacles: The forest environment is dynamic, with obstacles that can move (e.g., branches swaying due to wind). Model-based RL can build and continuously update a model of the environment, capturing these changes in real-time.
   • Terrain Changes: The drone might encounter varying terrain conditions such as open clearings, dense underbrush, or water bodies. A model-based approach allows the drone to simulate and plan its path considering these environmental variations.
2. Efficient Planning and Adaptation:
   • Simulated Experiences: Using the model, the drone can simulate numerous potential paths and their outcomes without physically navigating each one. This is particularly important in a forest where the wrong path could lead to collisions or getting stuck.
   • Real-Time Adjustments: The drone can adapt its route quickly if an obstacle suddenly appears or if there are changes in the terrain, thanks to the predictive power of the model.
3. Safety and Resource Optimization:
   • Collision Avoidance: The drone can predict and avoid potential collisions by simulating future states and planning accordingly.
   • Battery Efficiency: Efficient planning using a model ensures that the drone uses its battery power optimally, avoiding unnecessary detours or backtracking.

Why Model-Free RL is Not Suitable?

1. High Real-World Interaction Cost:
   • Risk of Damage: A model-free RL agent would require extensive trial-and-error to learn an optimal path. In a forest, this could lead to the drone frequently crashing into obstacles, causing damage and potentially leading to mission failure.
   • Time-Consuming: The learning process would be significantly slower as the drone would need to physically explore various paths multiple times to learn effective policies.
2. Inefficiency in Dynamic Environments:
   • Slow Adaptation: Model-free RL relies on accumulated experiences, making it less responsive to sudden changes in the environment. In a dynamic setting like a forest, this could result in the drone being unable to adapt quickly enough to avoid obstacles or take advantage of newly discovered paths.
3. Resource Constraints:
   • Battery Life: The extensive exploration required by model-free methods would drain the drone’s battery more rapidly, reducing the chances of successfully completing the mission.
   • Computational Limitations: While model-free RL might be less computationally intensive per step, the overall resource usage can become inefficient due to the sheer number of interactions needed to learn effectively.

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.