Understanding And Implementing Deep Q-Learning

Deep Q-Learning (DQL) is an exciting technique in the domain of reinforcement learning that utilizes artificial neural networks. Its successful application in the program AlphaGo brought it to the forefront. Now let's dive into understanding DQL and create an exemplified implementation using Python's PyTorch library.

Deep Q-Learning: Basics

Reinforcement learning is all about the interaction of an agent with the environment to maximize some notion of cumulative reward. Deep Q-Learning takes Q-Learning to the next level by combining it with deep neural networks.

In Deep Q-Learning, Q-values are approximated using Neural Networks. This way we can deal with larger environments and unseen states as Neural Networks can draw out patterns and learn about such environments.

import torch
import torch.nn as nn

class QNetwork(nn.Module):
    def __init__(self, state_size, action_size, seed):
        super(QNetwork, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.fc1 = nn.Linear(state_size, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, action_size)

    def forward(self, state):
        x = torch.relu(self.fc1(state))
        x = torch.relu(self.fc2(x))
        return self.fc3(x)

Deep Q-Learning: Key Components

Deep Q-Learning has two vital components:

1. Experience Replay - This addresses the issue of correlation between experiences. By storing these experiences in a replay buffer and randomly sampling experiences from the buffer to make decisions, the agent can learn from earlier experiences, and the problem of correlated experiences is alleviated.

from collections import namedtuple, deque
import random

class ReplayBuffer:
    def __init__(self, buffer_size, batch_size, seed):
        self.memory = deque(maxlen=buffer_size)
        self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "new_state", "done"])
        self.seed = random.seed(seed)
        self.batch_size = batch_size

    def store_experience(self, state, action, reward, next_state, done):
        experience = self.experience(state, action, reward, next_state, done)
        self.memory.append(experience)

    def sample(self):
        experiences = random.sample(self.memory, k=self.batch_size)

        states = torch.from_numpy(np.vstack([exp.state for exp in experiences if exp is not None])).float().to(device)
        actions = torch.from_numpy(np.vstack([exp.action for exp in experiences if exp is not None])).long().to(device)
        rewards = torch.from_numpy(np.vstack([exp.reward for exp in experiences if exp is not None])).float().to(device)
        next_states = torch.from_numpy(np.vstack([exp.new_state for exp in experiences if exp is not None])).float().to(device)
        dones = torch.from_numpy(np.vstack([exp.done for exp in experiences if exp is not None]).astype(np.uint8)).float().to(device)
        
        return (states, actions, rewards, next_states, dones)

2. Fixed Q-Targets - In standard Q-Learning, we update a guess with a guess. However, this can potentially lead to harmful correlations. To deal with this, the network parameters are separated into two sets. One set of parameters, θ, is used to select the best action and is updated regularly. The other set, θ-, is used to evaluate the best action and is updated less frequently.

class Agent():

    def __init__(self, state_size, action_size, seed):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = ReplayBuffer(BUFFER_SIZE, BATCH_SIZE, seed)
        self.t_step = 0
        # Initialize two Q-Networks
        self.qnetwork_local = QNetwork(state_size, action_size, seed)
        self.qnetwork_target = QNetwork(state_size, action_size, seed)

This brief insight into Deep Q-Learning aims to encourage further exploration into this fascinating and evolving technology. It is conscientious to mention that, although the implementations of the component are written in Python using the PyTorch library, the concepts remain relevant across all platforms and languages.