Key takeaways
Because this chapter was dense in terms of theory, we decided to add a small recap section. This chapter introduced RL as a core approach to enabling intelligent agents to learn from interaction with dynamic environments through trial and error, similar to how humans learn by acting, observing outcomes, and adjusting behavior. RL differs from supervised learning by focusing on learning from rewards rather than labeled data, and it is especially suited to tasks with delayed feedback and evolving decision sequences.
RL is a machine learning paradigm where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards. It learns through trial and error, balancing exploration (trying new actions) and exploitation (using known strategies).
In summary, we have these classes of methods:
- Model-free versus model-based RL:
- Model-free methods (e.g., DQN, REINFORCE) learn directly from interaction without modeling the environment...
