Discover the secrets of the Q-learning algorithm, where the Q-function takes center stage. With inputs of state and action, this powerful equation drives the learning process. Initially, the Q-values are all zero, but as we delve into the environment, these values dynamically update, refining our understanding.
Embark on the Journey: Step by Step
Step 1: Build the Q-Table
Construct a Q-table with columns representing actions and rows representing states. Initialize with zeros. As we delve into the robot example, the table boasts four columns and five rows, reflecting the available actions and conditions.
Steps 2 and 3: Choose and Perform
Navigate the unknown. Initially, the robot explores randomly, taking actions based on the yet-to-be-informed Q-table. Then, step by step, it gains confidence in estimating Q-values.
Steps 4 and 5: Evaluate and Update
Witness the power of observation. Next, evaluate outcomes and update the Q-function using the Bellman equation. This iterative process continues, refining the Q-Table and empowering the robot's learning.
Iterate. Explore. Learn. By following these steps, the Q-Table evolves, and knowledge flourishes. Embrace the Q-learning method, where insights shape intelligence and algorithms pave the way for success.