Master the Basics of Reinforcement Learning
Table of Contents:
- Introduction
- Understanding Reinforcement Learning
2.1 Definition of Reinforcement Learning
2.2 Training a Pong Playing Agent
- The Role of Supervised Learning
3.1 Training with a Coach
3.2 Penalizing Actions in Losses and Reinforcing Actions in Wins
- Challenges in Reinforcement Learning
4.1 Minimizing Error Function with Gradient Descent
4.2 Escaping Local Minima with Probabilistic Policies
- Incorporating Visual Information in Reinforcement Learning
5.1 Converting Image Input to Numeric Representation
5.2 Building a Small Deep Neural Network
- Training the Network to "See"
6.1 Visualizing the Weights of the First Neuron
6.2 Observing the Neural Network's Learning Process
- Policy Gradient and Proximal Policy Optimization (PPO)
- Applications of Reinforcement Learning
8.1 AlphaGo
8.2 Chat GPT
- Conclusion
Understanding Reinforcement Learning
Reinforcement learning is an approach to artificial intelligence (AI) where agents learn to make decisions Based on interactions with an environment. In this section, we will Delve into the concept of reinforcement learning and explore how to train a pong playing agent.
Reinforcement learning can be defined as a Type of machine learning where an agent learns through trial and error to maximize its cumulative reward in a given environment. In the Context of training a pong playing agent, we aim to develop a policy that takes the state of the world (ball and paddle positions) as input and generates an appropriate action (move up or down) based on that input.
To train our pong playing agent, we need to define a policy that maps the input state to an action. In this case, our policy will be represented as a neural network. The network will take the ball's X and Y positions, the paddle positions, and optionally the velocities as input. These inputs will be multiplied by weights and summed up. A sigmoid function will then convert the result to a value between 0 and 1. If the value is larger than 0.5, the agent will move up; otherwise, it will move down.
Initially, we start with random weights, resulting in a pong player that hides in the corner and does not perform well. However, we can use supervised learning to train our policy. We can receive coaching on which actions to take in specific scenarios and adjust our policy accordingly. By optimizing the weights to minimize the error between the desired actions and the outputs of the policy, we can improve the agent's performance.
While supervised learning with a coach is effective, it may not always be available or practical. In such cases, we can resort to reinforcement learning. In reinforcement learning, we do not have explicit coaching but instead learn from the consequences of our actions. If an action leads to a win, we reinforce it; if it leads to a loss, we penalize it. Over time, the agent learns which actions are more likely to result in a win and adjusts its policy accordingly.
Challenges arise in reinforcement learning due to the error function that needs to be minimized. Gradient descent, the method commonly used for optimization, can get stuck in local minima. To overcome this, we introduce randomness into the policy by making it probabilistic. This randomness allows the agent to explore a wider range of policies and helps it escape local minima.
Another challenge in reinforcement learning is the incorporation of visual information. While our previous examples used the ball and paddle positions as input, what if we only had a photo of the screen? In such cases, we need to train our policy to "see" the Relevant elements in the image. This can be achieved by converting the image into a numeric representation and training a deep neural network on this representation.
With reinforcement learning, we can train a small deep neural network to identify Patterns and make decisions in an environment. Each neuron in the network looks for specific patterns and contributes to the overall decision-making process. By training the network through millions of games, our simple network can learn how to "see" and improve its pong playing skills.
Policy gradient is a common approach to reinforcement learning, and it can be further enhanced by using Proximal Policy Optimization (PPO). These techniques have been applied to various applications, including the development of AlphaGo, a program that plays the game of Go, and Chat GPT, a language-based AI model. These applications highlight the effectiveness and potential of reinforcement learning in solving complex problems.
In conclusion, reinforcement learning provides a framework for agents to learn through interactions with their environment. By combining supervised learning, reinforcement learning, and the integration of visual information, we can train agents to make intelligent decisions in various domains. The possibilities and applications of reinforcement learning Continue to expand, making it an exciting area of research and development in the field of AI.