There are many benefits to using reinforcement learning (RL). However, these three often stand out.
Excels in complex environments
RL algorithms can be used in complex environments with many rules and dependencies. In the same environment, a human may not be capable of determining the best path to take, even with superior knowledge of the environment. Instead, model-free RL algorithms adapt quickly to continuously changing environments and find new strategies to optimize results.
Requires less human interaction
In traditional ML algorithms, humans must label data pairs to direct the algorithm. When you use an RL algorithm, this isn’t necessary. It learns by itself. At the same time, it offers mechanisms to integrate human feedback, allowing for systems that adapt to human preferences, expertise, and corrections.
Optimizes for long-term goals
RL inherently focuses on long-term reward maximization, which makes it apt for scenarios where actions have prolonged consequences. It is particularly well-suited for real-world situations where feedback isn't immediately available for every step, since it can learn from delayed rewards.
For example, decisions about energy consumption or storage might have long-term consequences. RL can be used to optimize long-term energy efficiency and cost. With appropriate architectures, RL agents can also generalize their learned strategies across similar but not identical tasks.