Explore the Power of Reinforcement Learning with My AI Agent
(AI Assistant သည် စမ်းသပ်မှု နှင့် အမှားမှ တစ်ဆင့် ဇယားကွက်ပေါ်ရှိ ပစ်မှတ်တစ်ခုသို့ ရောက်ရှိရန် သင်ယူသည့် Q-သင်ယူခြင်း ဥပမာတစ်ခု ဖြစ်သည်။)

I’ve built a cool AI agent that learns just like humans do—through trial and error! This Reinforcement Learning agent makes decisions based on rewards and penalties, improving its actions over time to reach a goal faster.

In this demo, the agent starts in a grid and works to find a target. At first, it’s unsure, but as it goes through more training episodes, it gets better with each step. You can even control the agent manually using the arrow keys.

This AI uses a technique called Q-learning, where it learns by storing actions and their rewards in a “Q-table.” With this, the agent gradually updates its strategy to make better decisions over time.

How Q-learning Works:

The Q-value is a score that tells the AI how good an action is in a given situation. The agent keeps adjusting these values as it learns from past experiences. Here's the formula behind it:

• Q(s, a) = Q(s, a) + α * (R + γ * max(Q(s', a')) - Q(s, a))

  Where:

  • Q(s, a): The current value of the action.

  • α (alpha): The learning rate, indicating how much feedback impacts decisions.

  • R: The reward for taking an action.

  • γ (gamma): The discount factor, determining how much future rewards matter.

  • max(Q(s', a')): The maximum value for the next action.

Try it out and watch the AI improve its decision-making abilities in real-time! It's a fun way to understand how machines can learn autonomously.