Key Algorithms for AI Agents
The algorithms that power AI agents span from classical decision-making to modern deep learning.
Decision Trees and Random Forests
Decision trees split data based on feature values to make predictions.
from sklearn.ensemble import RandomForestClassifier
# Train a random forest for agent decision-making
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
# Agent uses model to decide action
action = model.predict([current_state])[0]
- Decision Trees: Simple, interpretable, but prone to overfitting
- Random Forests: Ensemble of trees, more robust, better generalization
- Use case: Classification tasks in agent perception (spam detection, intent classification)
Neural Networks Fundamentals
Neural networks learn complex patterns through layers of connected neurons.
- Input layer: Receives raw data (percepts)
- Hidden layers: Extract features and patterns
- Output layer: Produces predictions (actions)
- Training: Backpropagation adjusts weights to minimize error
Q-Learning and Policy Gradients
Core reinforcement learning algorithms for agent training:
Q-Learning: Learn the value of state-action pairs
# Q-learning update rule
# Q(s, a) = Q(s, a) + alpha * (reward + gamma * max(Q(s', a')) - Q(s, a))
import numpy as np
Q = np.zeros((num_states, num_actions))
alpha = 0.1 # learning rate
gamma = 0.99 # discount factor
def update_q(state, action, reward, next_state):
best_next = np.max(Q[next_state])
Q[state, action] += alpha * (reward + gamma * best_next - Q[state, action])
Policy Gradients: Directly learn a policy (state → action mapping)
- More suitable for continuous action spaces
- Used in robotics, game playing
Natural Language Processing Basics
NLP enables agents to understand and generate human language:
- Tokenization: Breaking text into tokens
- Word embeddings: Dense vector representations (Word2Vec, GloVe)
- Transformers: Attention-based architecture (GPT, BERT)
- Key for: Chatbots, virtual assistants, text-based agents
Embeddings and Vector Representations
Embeddings convert discrete items into continuous vector spaces:
from openai import OpenAI
client = OpenAI()
# Generate embedding for text
response = client.embeddings.create(
model="text-embedding-3-small",
input="What is an AI agent?"
)
vector = response.data[0].embedding # 1536-dimensional vector
- Text embeddings: Represent meaning as vectors
- Similarity search: Find related content via cosine similarity
- RAG foundation: Retrieve relevant context for agent responses
Key Takeaways
- Decision trees/forests for structured decision-making
- Neural networks for complex pattern recognition
- Q-learning for reward-based agent training
- NLP and embeddings for language understanding
- These algorithms combine to create intelligent agents