Editing Reinforcement learning (section)

=== Sample Inefficiency ===
RL algorithms often require a large number of interactions with the environment to learn effective policies, leading to high computational costs and time-intensive to train the agent. For instance, [[OpenAI|OpenAI']]<nowiki/>s Dota-playing bot utilized thousands of years of simulated gameplay to achieve human-level performance. Techniques like experience replay and [[curriculum learning]] have been proposed to deprive sample inefficiency, but these techniques add more complexity and are not always sufficient for real-world applications.