PyTorch is a versatile deep learning library that offers a range of capabilities and functionalities for AI practitioners. Its alignment with Python programming paradigms create an essential tool for those who want a harmonious blend of coding and deep learning.
Its dynamic approach allows changes to be made as you continue working and offers flexibility when building and adjusting neural network models, while supervised learning helps make behavior simple based on the initial output. But what about learning more complex behaviors when data collection is inaccessible or difficult to obtain? This is where reinforcement learning comes into play.
What is Reinforcement Learning?
Reinforcement learning is a machine learning technique where an agent (model) learns to perform a task through repeated trial and error in a dynamic environment. This enables the agent to perform actions (decision-making) that receive rewards (feedback) for that specific task, which is why there is no need for human intervention.
Key elements of reinforcement learning
Agent: the decision-maker or model.
Environment: the surroundings in which the agent interacts.
Action: all the decisions or moves the agent makes.
Reward: feedback from the environment based on action taken.
Input: the initial state from which the model will start.
Output: solution to the problem.
State: situation in which the agent finds itself.
Supervised learning vs. reinforcement learning
While supervised learning relies on a training dataset with programmed or predefined answers, reinforcement learning functions through learned experiences, which enable it to study behavior.
Reinforcement learning makes decisions in a logical order, where the output relies on the state of the current input, allowing the next input to depend on the output of the previous input; thus, decisions are dependent on each other.
Supervised learning makes decisions solely based on the initial input, hence making decisions independent of each other.
How Does Reinforcement Learning Function?
The reinforcement learning model functions through the principle of learning optimal behavior by trial and error.
Policy: a strategy that is used by the agent to decide the next action based on its current state.
Reward function: a function that provides signaled feedback based on the state and action.
Value function: a function that evaluates the expected cumulative reward from the given state.
Model of the environment: representation of the environment that assists with planning through predicting future states and rewards.
Reinforcement Learning using PyTorch
In navigating complex environments, PyTorch enables dynamic adjustment, hence maximizing rewards. To put it simply, reinforcement learning is taught similar to how a child is taught through rewards and punishments.
PyTorch, as a deep learning library, is a helpful tool for reinforcement learning due to its flexibility and ability to execute tensor computations which are crucial for reinforcement algorithms.
Foundational PyTorch elements
Tensors: the main block of PyTorch in managing different operations and storage of data.
Computational graph: enables graph modification on the go, which is useful for models to ensure dynamic flow control. This feature applies to reinforcement learning in its experimentation of different strategies and adjustment of models, based on its performance in a dynamic environment.
Neural Network Module: offers pre-defined layers, loss functions, and optimization routines that allow users to combine neural architecture easily.
Utilities: it ranges from data handling to performance profiling, ensuring that developers can streamline the AI development process.
Example of reinforcement learning with PyTorch: CartPole balancing
The CartPole environment stimulates pole balancing on a cart which is done when an agent applies forces to the left or right to keep the pole balanced.
Start the environment: stimulate a pole balanced on a cart.
Build the Policy Network: generate a neural network to predict action based on the state of the environment.
Collecting data: as each episode goes, run the agent through its environment to gather actions, states, and rewards.
Calculating the policy gradient: use the data collected to evaluate gradients in order to improve the policy.
Updating the policy: modify the weights of neural networks based on the gradients in order to teach the agent preferable actions.
The power of reinforcement is that it learns to solve problems based on its experience from the outcomes of its actions rather than making decisions from direct intervention or assistance.
PyTorch enhances reinforcement learning by facilitating the building and training of these models for such tasks and actions, which provides an understanding of applying reinforcement learning techniques.
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About the Author: Tala Sammar is the Events and Content Marketing Intern at Ironhack based in Madrid, where she contributes to creating engaging content, assisting with blogs and events. With a background in International Relations, she is passionate about social justice, humanitarian development, and writing. Moreover, she is always expressing herself artistically, seeking to unlock layers of creativity. Her strong sense of empathy, specifically within the marketing field, is one of her greatest strengths. Catch her on LinkedIn here.
