Reinforcement Learning Overview

What is Reinforcement Learning?

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative reward. Unlike supervised learning, where the model learns from labeled data, RL focuses on learning from the consequences of actions.

Key Concepts

• Agent: The learner or decision maker.
• Environment: Everything the agent interacts with.
• Action: Choices made by the agent to interact with the environment.
• State: A snapshot of the environment at a given time.
• Reward: Feedback from the environment based on the action taken.
• Policy: The strategy that the agent employs to determine actions based on states.
• Value Function: A function that estimates the expected return of being in a state.

How Reinforcement Learning Works

The learning process in RL typically follows these steps:

            graph TD;
                A[Start] --> B{Is the episode finished?};
                B -- Yes --> C[End];
                B -- No --> D[Agent selects action];
                D --> E[Action taken in environment];
                E --> F[State and reward received];
                F --> B;
            

The agent interacts with the environment, receives feedback, and updates its policy accordingly. Over time, the agent learns the best actions to take to maximize its rewards.

Code Example

Here is a simple implementation of a reinforcement learning algorithm using Q-learning:

class QLearningAgent:
    def __init__(self, actions):
        self.q_table = {}
        self.actions = actions

    def choose_action(self, state):
        return self.actions[0]  # Simplified for example

    def learn(self, state, action, reward, next_state):
        # Q-learning formula
        self.q_table[state] = self.q_table.get(state, {})
        max_future_q = max(self.q_table.get(next_state, {}).values(), default=0)
        current_q = self.q_table[state].get(action, 0)
        new_q = current_q + 0.1 * (reward + 0.9 * max_future_q - current_q)
        self.q_table[state][action] = new_q
                

This class defines a simple Q-learning agent that can learn from its interactions with the environment.

FAQ