One thing I understand better now that I have completed the RLO is the concept of reinforcement learning and its applications. Before, I had a basic understanding of machine learning algorithms but wasn't familiar with the specific details and techniques involved in reinforcement learning. The RLO provided a comprehensive explanation of the key components of reinforcement learning, such as the agent, environment, state, action, reward, and policy. It also discussed various algorithms and approaches, including Q-learning and policy gradients. Now, I have a clearer understanding of how reinforcement learning can be used to train agents to make decisions and take actions in dynamic and uncertain environments.
