How does Deep Q-Network (DQN) improve on Q-learning?

Deep Q-Networks (DQN) have been pivotal in advancing reinforcement learning, particularly for applications with large and complex state spaces. Let's delve into the key innovations:

1. Neural Networks as Function Approximators:

   • In classical Q-learning, a Q-table is used to store the state-action values. However, this becomes infeasible in large state spaces. DQNs address this by using a neural network to approximate the Q-function: $Q(s, a; \theta)$, where $\theta$ represents the parameters of the neural network.
   • This allows DQNs to generalize across similar states, effectively handling large, continuous, or high-dimensional state spaces.

2. Experience Replay:

   • In traditional Q-learning, each experience tuple ((s, a, r, s')) is used immediately to update the Q-values, introducing strong correlations between consecutive updates. DQNs mitigate this by storing experiences in a replay buffer and sampling random mini-batches of experiences for training.
   • This technique breaks the correlation and leads to more stable learning by smoothing out changes in the data distribution.

3. Target Network:

   • DQNs use a separate target network to compute the target Q-values. The target network is updated less frequently than the primary network, which helps in stabilizing the updates.
   • This involves maintaining two networks: the primary network with parameters $\theta$ and the target network with parameters $\theta^-$. The target network is updated to the primary network's parameters at regular intervals.

Practical Application:

• DQNs have been famously applied to play Atari games, where they achieved human-level performance. In these environments, the state space is represented by pixel data from the game screen, which is high-dimensional and complex.

Code Example:

# Pseudo-code for experience replay mechanism
class ReplayBuffer:
    def __init__(self, capacity):
        self.buffer = deque(maxlen=capacity)

    def add(self, experience):
        self.buffer.append(experience)

    def sample(self, batch_size):
        return random.sample(self.buffer, batch_size)

Mermaid Diagram:

graph TD;
    A[Environment] -->|State| B[Primary Network];
    B -->|Action| A;
    A -->|Reward, Next State| C[Replay Buffer];
    C -->|Sampled Experience| B;
    B -->|Parameters| D[Target Network];
    D -->|Target Q-values| B;

For further reading, you can explore this paper on DQNs by DeepMind, which provides an in-depth view of these innovations.