Learning Through Rewards - Reinforcement Learning

Reinforcement learning is a method in which AI learns by performing actions within an environment and receiving rewards as a result.

Mathematically, the basic concepts of reinforcement learning are structured as follows:

1. Agent: The learning AI itself

2. Environment: The space where the AI operates

3. Action: The movements the AI can choose

4. Reward: An evaluation of how favorable the AI's actions are

5. State: The current situation the AI is in

For example, using reinforcement learning to develop a game AI would proceed as follows:

The AI gradually discovers better strategies through trial and error.

Reinforcement learning is largely categorized into two approaches.

This is a method where the AI directly learns what actions to take.

The AI learns how to choose the best action in a specific state, and when combined with deep learning, it can deliver powerful performance.

Examples of situations where the AI would learn to choose optimal actions include:

• A robotic arm learning optimal movements

• A game AI learning play strategies

• An autonomous vehicle optimizing its driving route

This approach involves calculating the value of each action to choose the one that maximizes rewards.

The AI learns "How beneficial is this action?" and prioritizes actions that offer higher rewards.

Examples of situations where the AI would learn to choose optimal actions include:

• A chess AI learning how to find the best moves

• A stock trading AI learning strategies to maximize profit

• A logistics optimization AI learning the best delivery routes

Reinforcement learning is a powerful technique, but it comes with certain drawbacks.

Since the AI learns through trial and error, the learning process can be slow and requires a lot of data to be effective.

If the rewards are set incorrectly, the AI might reinforce undesirable behaviors. For example, if an autonomous vehicle AI is rewarded solely for speed, it might ignore traffic signals.

While reinforcement learning is potent in simulation environments, real-world applications require consideration of complex variables and physical environments.

To overcome these limitations, reinforcement learning is being developed in conjunction with deep learning for more sophisticated learning capabilities.

In the next session, we'll tackle a simple quiz to review the material covered so far.