This is a type of machine learning where an agent learns the environment by interacting and makes decisions for further work. The actions will be taken on the environment and based on the results of the actions, the agent receives feedback in the form of rewards or penalties.
Let us dive into a real-time example to understand this better. I am training a robot to play a Maze Game.
Imagine that robot needs to reach the goal location. The robot has no knowledge of the maze layout but it knows to move forward, backwards, left and right.
So, what are the steps?
- Initialisation
In the beginning, the robot has random moves. It doesn’t know in what direction to move. So, the robot is our agent here, and the environment it moves is the place where it operates.
2. Exploration and Exploitation
The Robot takes random actions by exploring the maze. The robot may get lost at the start, there is a possibility.
3. Reward Feedback
If the Robot moves correctly according to the goal it receives positive feedback. If in case, the robot hits the wall or anything it gets a penalty. Based on such feedback the robot learns better.
4. Learning
So, the robot learns reinforcement learning like Q-learning or Deep Q Networks, to update its strategy based on the rewards it received. As a result, the positive ones are only considered by the robot.
5. Policy Improvement
As the robot searches and receives positive rewards, its action strategy policy is improved. This cumulates the positive approach.
6. Optimal Policy
After some iterations, the robot comes to an optimal policy. This is a strategy that improves navigation and it reaches the goal with the highest probability.
To conclude, as the robot undergoes reinforcement learning it knows to navigate intelligently, finding the most efficient path to reach its goal. This way, it improves decision-making skills. This is something similar to trial and error concept. It learns from the outcomes of actions and adjusts the behaviour to maximise the rewards in the maze environment.
This example demonstrates how reinforcement learning enables an agent (the robot) to learn from experience and optimize its actions in a dynamic environment to achieve a specific goal.
So, that’s it for the day! Thanks for your time in reading my article. Tell me your feedback or views in the comments section.
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