Deep Q-Learning (DQN)

Overview

As an extension of the Q-learning, DQN's main technical contribution is the use of replay buffer and target network, both of which would help improve the stability of the algorithm.

Original papers:

• Human-level control through deep reinforcement learning

Implemented Variants

Variants Implemented	Description
dqn.py, docs	For classic control tasks like CartPole-v1.
dqn_atari.py, docs	For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.

Below are our single-file implementations of DQN:

dqn.py

The dqn.py has the following features:

• Works with the Box observation space of low-level features
• Works with the Discrete action space
• Works with envs like CartPole-v1

Implementation details

dqn.py includes the 11 core implementation details:

dqn_atari.py

The dqn_atari.py has the following features:

• For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.
• Works with the Atari's pixel Box observation space of shape (210, 160, 3)
• Works with the Discrete action space