What Makes an Agent “Self-Improving
Types of Self-Improving Agents
Example in Practice
Self-Improving Agent using Deep Learning (Neural Network)
Self-Improving Agent using Evolutionary Algorithms
Self-Improving Agent using Reinforcement Learning (Q-Learning)
An AI agent can be called a self-improving agent when it has the ability to learn from its own experience and environment feedback, instead of only following fixed rules or pre-programmed logic
What Makes an Agent “Self-Improving”
Learning from Data & Feedback
The agent refines its performance by analyzing outcomes (success/failure, rewards/penalties).
Example: A chatbot improving its answers after seeing user ratings or corrections.
Adaptive Behavior
The agent changes strategies when conditions change.
Example: A trading agent adjusting investment rules after detecting new market patterns.
Reinforcement Learning (RL)
A common method where agents receive rewards for good actions and penalties for bad ones, gradually improving decisions.
Like a child learning not to touch fire after getting burned once.
Continuous Knowledge Updating
Uses online learning, fine-tuning, or federated learning to keep updating its knowledge base.
Example: Healthcare AI that keeps learning from new patient records.
Meta-Learning (“Learning to Learn”)
Goes beyond specific tasks, improving the ability to learn new tasks faster in the future.
🔹 Types of Self-Improving Agents
Reactive self-improving agents: Adjust to immediate feedback (like customer chat sentiment).
Proactive self-improving agents: Explore new strategies before problems arise (like predictive maintenance in factories).
Autonomous self-improving agents: Use memory, planning, and reasoning to refine their own decision policies.
🔹 Example in Practice
In Motoshare (vehicle rental SaaS) → An AI agent can track which rental packages customers choose, learn why cancellations happen, and automatically adjust pricing or recommendations.
In MyHospitalNow (doctor portal) → A self-improving scheduling agent can learn peak booking times and optimize appointment slots dynamically.
Self-Improving Agent using Reinforcement Learning (Q-Learning)
# Install necessary libraries
# pip install numpy
import numpy as np
import random
# Define the environment (Gridworld)
grid_size = 5 # 5x5 grid
goal_state = (4, 4)
obstacles = [(1, 1), (2, 2), (3, 3)] # Blocked states
actions = ['up', 'down', 'left', 'right']
# Q-table (state-action value table)
Q = np.zeros((grid_size, grid_size, len(actions)))
# Learning parameters
alpha = 0.1 # Learning rate
gamma = 0.9 # Discount factor
epsilon = 0.1 # Exploration rate
episodes = 1000 # Number of episodes for training
# Define reward function
def reward(state):
if state == goal_state:
return 100 # Goal state reward
elif state in obstacles:
return -10 # Penalty for hitting obstacles
else:
return -1 # Normal move cost
# Define actions and their effects on position
action_effects = {
'up': (-1, 0), # move up
'down': (1, 0), # move down
'left': (0, -1), # move left
'right': (0, 1) # move right
}
# Choose an action based on epsilon-greedy strategy
def choose_action(state):
if random.uniform(0, 1) < epsilon:
return random.choice(range(len(actions))) # Explore
else:
return np.argmax(Q[state[0], state[1]]) # Exploit
# Train the agent
for episode in range(episodes):
state = (0, 0) # Starting state (top-left corner)
while state != goal_state:
action_idx = choose_action(state)
action = actions[action_idx]
# Apply action
effect = action_effects[action]
next_state = (state[0] + effect[0], state[1] + effect[1])
# Stay within grid boundaries
next_state = (max(0, min(next_state[0], grid_size - 1)), max(0, min(next_state[1], grid_size - 1)))
# Get reward for the next state
r = reward(next_state)
# Q-value update (Q-learning formula)
Q[state[0], state[1], action_idx] = Q[state[0], state[1], action_idx] + alpha * (r + gamma * np.max(Q[next_state[0], next_state[1]]) - Q[state[0], state[1], action_idx])
# Update state
state = next_state
print("Training complete.")
# Testing the agent after training
state = (0, 0)
steps = 0
while state != goal_state and steps < 50:
action_idx = np.argmax(Q[state[0], state[1]]) # Choose the best action
action = actions[action_idx]
effect = action_effects[action]
state = (state[0] + effect[0], state[1] + effect[1])
state = (max(0, min(state[0], grid_size - 1)), max(0, min(state[1], grid_size - 1))) # Stay in grid
print(f"Step {steps+1}: Agent moved to {state}")
steps += 1
Self-Improving Agent using Deep Learning (Neural Network)
import pandas as pd
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# Sample customer data (age, tenure, balance, churn status)
data = {
'age': [22, 25, 27, 32, 45, 50, 55, 30],
'tenure': [1, 2, 3, 4, 5, 6, 7, 8],
'balance': [2000, 3000, 1500, 4000, 5000, 6000, 7000, 8000],
'churn': [0, 0, 1, 0, 1, 0, 1, 0] # 0 = stay, 1 = churn
}
# Create DataFrame
df = pd.DataFrame(data)
# Features (age, tenure, balance) and target (churn)
X = df[['age', 'tenure', 'balance']].values # Independent variables (features)
y = df['churn'].values # Dependent variable (target)
# Split data into training and testing sets (80% for training, 20% for testing)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Normalize the features (important for neural network performance)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Build a simple Neural Network (DNN) model for churn prediction
model = Sequential()
model.add(Dense(64, input_dim=3, activation='relu')) # Input layer with 3 features
model.add(Dense(32, activation='relu')) # Hidden layer with 32 neurons
model.add(Dense(1, activation='sigmoid')) # Output layer with 1 neuron (binary classification)
# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Train the model (Self-improvement through learning)
model.fit(X_train, y_train, epochs=100, batch_size=2) # Train for 100 epochs
# Evaluate the model on test data
loss, accuracy = model.evaluate(X_test, y_test)
print(f"Model Accuracy: {accuracy:.2f}")
# AI Agent Decision Class
class ChurnPredictionAgent:
def __init__(self, model, scaler):
self.model = model
self.scaler = scaler
def predict_churn(self, user_data):
"""Predict whether the user will churn (1) or stay (0)."""
user_scaled = self.scaler.transform([user_data]) # Scale the input data
churn_prob = self.model.predict(user_scaled) # Get the predicted churn probability
return churn_prob[0][0] > 0.5 # If probability > 0.5, predict churn
def decision(self, user_data):
"""Make a decision based on churn prediction."""
if self.predict_churn(user_data):
return "This customer is predicted to churn."
else:
return "This customer is predicted to stay."
# Create an instance of the ChurnPredictionAgent class
churn_agent = ChurnPredictionAgent(model, scaler)
# Example: Predicting churn for a new customer
new_customer_data = [35, 4, 4500] # New customer data (age, tenure, balance)
decision = churn_agent.decision(new_customer_data) # Get AI agent's decision
print(decision)
Self-Improving Agent using Evolutionary Algorithms
import numpy as np
import random
from deap import base, creator, tools, algorithms
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# Sample customer data for churn prediction (same as the previous example)
data = {
'age': [22, 25, 27, 32, 45, 50, 55, 30],
'tenure': [1, 2, 3, 4, 5, 6, 7, 8],
'balance': [2000, 3000, 1500, 4000, 5000, 6000, 7000, 8000],
'churn': [0, 0, 1, 0, 1, 0, 1, 0]
}
# Create DataFrame
df = pd.DataFrame(data)
# Features (age, tenure, balance) and target (churn)
X = df[['age', 'tenure', 'balance']].values
y = df['churn'].values
# Normalize the features
scaler = StandardScaler()
X = scaler.fit_transform(X)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Define the evaluation function for the genetic algorithm
def evaluate(individual):
model = Sequential()
model.add(Dense(64, input_dim=3, activation='relu', weights=[np.array(individual[:3]), np.zeros(64)]))
model.add(Dense(1, activation='sigmoid', weights=[np.array(individual[3:67]), np.zeros(1)]))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(X_train, y_train, epochs=5, batch_size=2, verbose=0)
# Evaluate on the test set
loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
return (accuracy,)
# Create the Genetic Algorithm framework
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_float", random.uniform, -1, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=67) # Neural network parameters
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.2)
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("evaluate", evaluate)
# Create a population of agents (individuals)
population = toolbox.population(n=10)
# Run the genetic algorithm to optimize the agents
algorithms.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.2, ngen=10, verbose=True)
# The best individual (agent) after evolution
best_agent = tools.selBest(population, 1)[0]
print("Best agent:", best_agent)
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