In reinforcement learning for helicopter control, a reward function scores how good each possible trajectory is. The reward may penalize crashes heavily and reward safe landings, while trading off smoothness, landing location, ride roughness, and other desiderata.

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In reinforcement learning, the machine goes through trial and error processes where it is rewarded/penalized for the actions it performs. The machine's goal is to maximize the total reward by leveraging previous attempts to make the next decision. The model starts from completely random trials and eventually leads to something with very sophisticated tactics and skills. Compared to supervised/unsupervised learning, reinforcement learning is much more advanced. A reinforcement model will continuously learn, unlike supervised/unsupervised models which all have an endpoint after the training and test data phases.

Reinforcement Learning

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

A good way to understand reinforcement learning would be to think about how autonomous vehicles work. Instead of programming all of the rules it should follow (such as road signs, pedestrians, right of way, etc), it is much more easier and efficient to implement a reward/penalty system where the car learns all of the rules that it is supposed to abide by. Furthermore, it would be unreasonable to program individual rules because accidents can happen in unpredictable ways and rules/regulations are always changing and varies in different parts of the world. So, it is much better for autonomous cars to be always learning better ways to make decisions.

Reinforcement Learning Example - Autonomous Vehicles

Video games often involve completing levels, with increasing difficulty as you go through the game. In each level, you go in completely blind and make decisions that will either lead to you passing that level or failing. If you fail, you will now understand more about that particular level that will influence your decisions when you retry. In other words, you will understand what not to do, and try another approach. If you pass, you will understand what to do in future levels. This process is repeated until success. This is very similar to how reinforcement learning works. Rewards are given for successes, indicating that the decision(s) made are correct and should be learned for future use. Penalties are given for a failure, indicating that the decision(s) made should not be retried and instead a new approach should be used.

Reinforcement Learning Analogy - Video Games

Theobald, O. (2017). Machine learning for absolute beginners: a plain English introduction (2nd ed.). Scatterplot Press.

Online pdf: http://bmansoori.ir/book/Machine%20Learning%20For%20Absolute%20Beginners.pdf

Machine Learning for Absolute Beginners

Deep learning analyses a training set, identifies complex patterns and applies them to new data. 

Reinforcement learning works sequentially in an unknown environment━taking an action, evaluating the rewards, and adjusting the following actions accordingly.

Deep learning and reinforcement learning complement each other:

 - Reinforcement learning algorithms manage the sequential process of taking an action, evaluating the result, and selecting the next best action. However, they need a good mechanism to select the best action based on previous interactions.
 - Deep learning can be that mechanism━it is the most powerful method available today to learn the best outcome based on previous data.

 

Deep Learning vs. Reinforcement Learning

The fundamental concepts that form the basis of reinforcement learning are:

 - Agent
 - Environment
 - Action
 - State
 - Reward

The agent learns through repeated interaction with the environment. To be successful, the agent needs to:
 - Learn the interaction between states, actions, and subsequent rewards.
 - Determine which action will provide the optimal outcome.

Fundamental Concepts for Reinforcement Learning

(Encourage to continuously enrich and edit this node for sharing papers and their proposed useful ideas.)
1.Learning Action Representations for Reinforcement Learning Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas 

Reinforcement Learning Refrence and Cutting-edge Ideas

A team is developing a program to play a complex board game against human opponents. The program has no pre-existing data of past games to learn from. Instead, it is designed to learn by playing against itself repeatedly. After each game, the program receives a positive signal if it wins and a negative signal if it loses. Over time, it is expected to discover winning strategies on its own. Which of the following statements best analyzes why this learning approach is suitable for this task?

This scenario describes a classic reinforcement learning problem. Identify the Agent, Environment, State, Action, and Reward within this context. Then, explain how the reward structure incentivizes the agent to achieve the desired outcome of finding the exit quickly.

Robot Maze Navigation Strategy

A company is developing a system to recommend personalized news articles. The system's goal is to continuously adapt to a user's evolving interests. Two strategies are proposed:

**Strategy A:** Collect a massive, one-time dataset of user profiles and the articles they clicked on in the past. Train a model on this static dataset to predict which articles a new user is likely to click.

**Strategy B:** Design a system that learns through direct interaction. For each article it recommends, it receives a positive signal if the user clicks it and a negative signal if they ignore it. The system's objective is to maximize the positive signals it receives over time.

Evaluate these two strategies. Which one is better suited for creating a system that adapts to a user's *evolving* interests? Justify your evaluation by comparing the core mechanics of each strategy and their implications for long-term personalization.

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