Introduction To Reinforcement Learning

What is Reinforcement Learning?

Reinforcement Learning (RL) is a subfield of machine learning focused on how agents should take actions in an environment to maximize cumulative reward. Unlike supervised learning, where the model learns from a labeled dataset, in reinforcement learning, the agent learns through trial and error, receiving feedback in the form of rewards or penalties.

Key Concepts in Reinforcement Learning

There are several key concepts that form the foundation of reinforcement learning:

Agent: The learner or decision maker that interacts with the environment.
Environment: The external system that the agent interacts with.
Action: The choices made by the agent that affect the state of the environment.
State: A representation of the current situation of the agent in the environment.
Reward: Feedback from the environment based on the action taken by the agent; it can be positive or negative.
Policy: A strategy that the agent employs to determine the next action based on the current state.
Value Function: A function that estimates the expected cumulative reward that can be obtained from a state.

How Reinforcement Learning Works

The process of reinforcement learning can be summarized in the following steps:

The agent observes the current state of the environment.
The agent selects an action based on its policy.
The action is executed, leading to a new state of the environment.
The agent receives a reward (or penalty) from the environment based on the action taken.
The agent updates its knowledge (policy) based on the reward received.

Example of Reinforcement Learning: The CartPole Problem

One classic example of reinforcement learning is the CartPole problem, where the objective is to balance a pole on a moving cart. The agent can apply a force to the left or right to keep the pole balanced. The state of the environment includes the position and velocity of the cart, as well as the angle and angular velocity of the pole.

Code Example

Here’s a simple implementation using Python and the Keras library:

import gym
from keras.models import Sequential
from keras.layers import Dense
import numpy as np

# Create the CartPole environment
env = gym.make('CartPole-v1')

# Define a simple neural network model
model = Sequential()
model.add(Dense(24, input_dim=4, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(2, activation='linear'))
model.compile(loss='mse', optimizer='adam')

Conclusion