Deep Q-Learning (DQN)

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:

• Playing Atari with Deep Reinforcement Learning
• Human-level control through deep reinforcement learning

Our single-file implementations of DQN:

• dqn.py
  • Works with the Box observation space of low-level features
  • Works with the Discerete action space
  • Works with envs like CartPole-v1
• dqn_atari.py
  • 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 Discerete action space