The promising nature of Reinforcement Learning lies in its ability to autonomously learn complex behaviors, adapt to changes, optimize long-term goals, and integrate with other advanced ML techniques.
These characteristics make RL a versatile and powerful approach for a wide range of real-world problems.
This is a trained model of an agent playing LunarLander using the Proximal Policy Optimization algorithm.