Reinforcement Learning Basics

AI that learns by doing.

What is Reinforcement Learning?

Agent learns by interacting with environment.

Key Idea: Actions → Rewards/Penalties → Learning

Like training a dog with treats!

Key Concepts

Agent: The learner (AI)
Environment: The world
State: Current situation
Action: What agent can do
Reward: Feedback from environment

Example - Robot Navigation

State: Robot's position in room
Actions: Move forward, turn left, turn right
Reward: +10 for reaching goal, -1 for hitting wall

Q-Learning

Simple RL algorithm:

import numpy as np

# Q-table: State x Action → Expected reward
Q = np.zeros((num_states, num_actions))

# Hyperparameters
learning_rate = 0.1
discount = 0.95
epsilon = 0.1  # Exploration rate

for episode in range(1000):
    state = env.reset()
    done = False
    
    while not done:
        # Choose action (epsilon-greedy)
        if np.random.random() < epsilon:
            action = env.action_space.sample()  # Explore
        else:
            action = np.argmax(Q[state])  # Exploit
        
        # Take action
        next_state, reward, done, _ = env.step(action)
        
        # Update Q-value
        old_q = Q[state, action]
        next_max = np.max(Q[next_state])
        new_q = old_q + learning_rate * (reward + discount * next_max - old_q)
        Q[state, action] = new_q
        
        state = next_state

# Use learned policy
state = env.reset()
while not done:
    action = np.argmax(Q[state])
    state, reward, done, _ = env.step(action)

OpenAI Gym

Standard RL environment:

import gym

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

# Reset environment
state = env.reset()

for _ in range(1000):
    env.render()
    
    # Take random action
    action = env.action_space.sample()
    
    # Get result
    next_state, reward, done, info = env.step(action)
    
    if done:
        break

env.close()

Deep Q-Network (DQN)

Q-Learning with neural network:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from collections import deque
import random

class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95  # Discount rate
        self.epsilon = 1.0  # Exploration rate
        self.epsilon_decay = 0.995
        self.epsilon_min = 0.01
        
        self.model = self._build_model()
    
    def _build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=0.001))
        return model
    
    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))
    
    def act(self, state):
        if np.random.random() <= self.epsilon:
            return random.randrange(self.action_size)
        q_values = self.model.predict(state)
        return np.argmax(q_values[0])
    
    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target += self.gamma * np.amax(self.model.predict(next_state)[0])
            
            target_f = self.model.predict(state)
            target_f[0][action] = target
            
            self.model.fit(state, target_f, epochs=1, verbose=0)
        
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

# Train DQN
agent = DQNAgent(state_size=4, action_size=2)

for episode in range(1000):
    state = env.reset()
    state = np.reshape(state, [1, 4])
    
    for time in range(500):
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        next_state = np.reshape(next_state, [1, 4])
        
        agent.remember(state, action, reward, next_state, done)
        state = next_state
        
        if done:
            print(f"Episode: {episode}, Score: {time}")
            break
    
    if len(agent.memory) > 32:
        agent.replay(32)

Policy Gradient

Learn policy directly:

# Instead of Q-values, output action probabilities
model = Sequential([
    Dense(24, input_dim=state_size, activation='relu'),
    Dense(24, activation='relu'),
    Dense(action_size, activation='softmax')  # Probabilities
])

# Sample action from probability distribution
probs = model.predict(state)[0]
action = np.random.choice(action_size, p=probs)

Applications

• Game playing (AlphaGo, Atari)
• Robotics
• Self-driving cars
• Resource management
• Trading algorithms
• Recommendation systems

Challenges

• Sparse rewards
• Exploration vs exploitation
• Sample inefficiency
• Stability

Remember

• RL learns through trial and error
• Q-Learning for discrete actions
• DQN for complex environments
• Requires lots of episodes