|
|
@@ -42,7 +42,7 @@ So far, the agents we've discussed, although increasingly complex in functionali
|
|
|
|
|
|
This is the core idea of **Learning Agents**, and **Reinforcement Learning (RL)** is the most representative path to realizing this idea. A learning agent contains a performance element (the various types of agents we discussed earlier) and a learning element. The learning element continuously modifies the performance element's decision-making strategy by observing the results of the performance element's actions in the environment.
|
|
|
|
|
|
-Imagine an AI learning to play chess. It might start by making random moves, but when it finally wins a game, the system gives it a positive reward. Through extensive self-play, the learning element gradually discovers which moves are more likely to lead to ultimate victory. AlphaGo is a milestone achievement of this philosophy. In the complex game of Go, through reinforcement learning, it discovered many effective strategies that surpass existing human knowledge.
|
|
|
+Imagine an AI learning to play chess. It might start by making random moves, but when it finally wins a game, the system gives it a positive reward. Through extensive self-play, the learning element gradually discovers which moves are more likely to lead to ultimate victory. AlphaGo Zero is a milestone achievement of this philosophy. In the complex game of Go, through reinforcement learning, it discovered many effective strategies that surpass existing human knowledge.
|
|
|
|
|
|
From simple thermostats to cars with internal models, to navigation that can plan routes, to decision-makers who know how to weigh pros and cons, and finally to learners who can self-evolve through experience. This evolutionary path demonstrates the development trajectory that traditional artificial intelligence has undergone in building machine intelligence. They have laid a solid and necessary foundation for our understanding of more cutting-edge agent paradigms today.
|
|
|
|
|
|
@@ -639,4 +639,4 @@ Have questions while learning this chapter? Want to share insights with other le
|
|
|
|
|
|
**💡 Tip:** There's also a comment section at the bottom of each page for direct discussion!
|
|
|
|
|
|
----
|
|
|
+---
|
jjyaoao