⚡️Q learning is a combination of reinforcement learning and pathfinding algorithms.
🔑The Q table is like a cheat sheet that helps the agent make the best decisions in different states.
🌟Q learning goes beyond immediate rewards and considers long-term consequences.
🚀Over time, Q learning improves its accuracy and navigates the environment effectively.
💡Traditional large language models have limitations, such as data dependency and static knowledge.
What is Q learning?
—Q learning is a type of machine learning used in reinforcement learning. It combines reinforcement learning and pathfinding algorithms to optimize decision-making.
What is the Q table?
—The Q table is like a cheat sheet that tells the agent which actions to take in different states. It helps the agent learn and make better decisions over time.
How does Q learning consider long-term consequences?
—Q learning uses a formula that considers the current reward and potential future rewards. This ensures that the agent doesn't just focus on immediate rewards but also considers long-term outcomes.
Does Q learning improve over time?
—Yes, with enough exploration and learning, the Q table becomes more accurate, and the agent improves at predicting actions that yield the highest rewards.
What are the limitations of traditional large language models?
—Traditional large language models have limitations such as data dependency, static knowledge, context understanding, and biases.
00:00This video introduces Q learning and its potential to revolutionize large language models and AI models.
02:23Q learning involves the environment, agent, states, actions, the Q table, learning by doing, updating the Q table, and improving over time.
06:08Traditional large language models have limitations, including data dependency and lack of context understanding.
08:48Q learning is being explored as a future solution to overcome the limitations of traditional large language models.
