DQN-tensorflow
TensorFlow implementation of Deep Q-Networks (DQN) for human-level control in reinforcement learning environments.
What is DQN-tensorflow?
DQN-tensorflow is an open-source implementation of Deep Q-Networks (DQN) using TensorFlow, based on the groundbreaking DeepMind paper 'Human-Level Control through Deep Reinforcement Learning'. It enables training reinforcement learning agents to play Atari games and other environments at human-level performance by combining Q-learning with deep neural networks. The implementation includes key stabilization techniques like experience replay and fixed target networks.
Machine learning researchers and practitioners interested in deep reinforcement learning, particularly those wanting to experiment with DQN algorithms in TensorFlow. It's also suitable for students learning about value-based reinforcement learning methods.
Provides a clear, well-structured TensorFlow implementation of the foundational DQN algorithm with essential stabilization techniques, making it easier to understand and experiment with deep reinforcement learning compared to building from scratch.
Overview
Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning
Use Cases
Best For
- Implementing DQN algorithms for Atari game playing
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