Reinforcement Learning (Deep Q Networks, A3C)

Reinforcement Learning (Deep Q Networks, A3C)

Reinforcement Learning (RL) is a branch of machine learning that focuses on training agents to make a sequence of decisions. The agent learns to perform actions based on reward feedback from the environment. Two popular methods in RL are Deep Q Networks (DQN) and Asynchronous Advantage Actor-Critic (A3C).

Deep Q Networks (DQN)

DQN is a value-based RL algorithm that combines Q-Learning with deep neural networks. It was introduced by DeepMind in 2013 and has been a significant milestone in the field of RL.

Key Features

  • Experience Replay: DQN uses a technique called experience replay where it stores the agent’s experiences at each time step in a data set called a replay buffer. During training, random mini-batches from the replay buffer are used, which breaks the correlation between consecutive samples, stabilizing the learning process.

  • Target Network: DQN uses a separate network to estimate the Q-value for the next state in the update equation. This target network has the same architecture as the original network but with frozen parameters. The parameters are updated from the original network periodically, which helps to stabilize learning.

Asynchronous Advantage Actor-Critic (A3C)

A3C is a policy-based RL algorithm that uses an actor-critic architecture. It was introduced by DeepMind in 2016 as an improvement over the earlier Advantage Actor-Critic (A2C) method.

Key Features

  • Asynchronous Updates: A3C uses multiple instances of the environment running on different threads to collect experiences. These experiences are used to update the global network asynchronously, which leads to more diverse data and faster learning.

  • Actor-Critic Architecture: A3C uses a model with two components: an actor that decides which action to take, and a critic that estimates the value of the state. This combination helps to reduce the variance of updates and accelerates learning.


Both DQN and A3C have been used in a variety of applications, including game playing, robotics, and autonomous driving. DQN was the first algorithm to achieve human-level performance on several Atari 2600 games, while A3C has been used to train agents in complex 3D environments.

Further Reading

  • Q-Learning
  • Policy Gradient Methods
  • Actor-Critic Methods
  • Experience Replay
  • Deep Learning
  • Machine Learning

Reinforcement Learning, with its methods like DQN and A3C, is a rapidly evolving field. It’s a key component in the toolbox of any data scientist working on problems where sequential decision making is required.