Deep Reinforcement Learning for Beginners: Video Tutorial Series

Introduction to Reinforcement Learning

Reinforcement Learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.

Key Concepts in Reinforcement Learning

Before diving into deep reinforcement learning, it's important to understand the foundational concepts like states, actions, policies, rewards, and value functions.

import gymnasium as gym
import numpy as np

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

# Reset the environment
state = env.reset()

# Take a random action
action = env.action_space.sample()
next_state, reward, done, info, _ = env.step(action)

print(f"State: {state}")
print(f"Action: {action}")
print(f"Reward: {reward}")
print(f"Done: {done}")

Deep Q-Networks (DQN)

In this section, we'll examine how deep neural networks can be used to approximate Q-values in reinforcement learning problems with large state spaces.

What are Deep Q-Networks?

Deep Q-Networks combine Q-learning with deep neural networks to handle high-dimensional state spaces. Instead of maintaining a table of Q-values, we use a neural network to approximate the Q-function.

import torch
import torch.nn as nn
import torch.optim as optim

class DQN(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(DQN, self).__init__()
        self.network = nn.Sequential(
            nn.Linear(input_dim, 128),
            nn.ReLU(),
            nn.Linear(128, 128),
            nn.ReLU(),
            nn.Linear(128, output_dim)
        )
        
    def forward(self, x):
        return self.network(x)

# Example instantiation
state_dim = 4  # CartPole has 4 state dimensions
action_dim = 2  # CartPole has 2 possible actions
model = DQN(state_dim, action_dim)
optimizer = optim.Adam(model.parameters(), lr=0.001)

Experience Replay

One of the key innovations in DQN is experience replay, which helps break correlations between consecutive samples:

class ReplayBuffer:
    def __init__(self, capacity):
        self.capacity = capacity
        self.buffer = []
        self.position = 0
        
    def push(self, state, action, reward, next_state, done):
        if len(self.buffer) < self.capacity:
            self.buffer.append(None)
        self.buffer[self.position] = (state, action, reward, next_state, done)
        self.position = (self.position + 1) % self.capacity
        
    def sample(self, batch_size):
        batch = random.sample(self.buffer, batch_size)
        state, action, reward, next_state, done = map(np.stack, zip(*batch))
        return state, action, reward, next_state, done
    
    def __len__(self):
        return len(self.buffer)

Policy Gradient Methods

While DQN focuses on learning value functions, policy gradient methods directly optimize the policy.