Reinforcement learning (RL) is a rising star approach for developing autonomous robot agents.
The essence of RL is training agents based on policies that reward desirable outcomes, which leads to increased adaptability to operational scenarios.
Through iterations, robots refine their decision-making, optimizing actions based on rewards and penalties. This method provides robots with the flexibility to handle unpredictable situations, enhancing their efficiency and effectiveness in real-world tasks.
To learn about RL with Duckietown, check out the resources below.