danaugrs/huskarl
A TensorFlow 2.0-based deep reinforcement learning framework for modular and fast prototyping of RL algorithms.
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Huskarl is a framework for deep reinforcement learning focused on modularity and fast prototyping. It is built on TensorFlow 2.0 using the tf.keras API and enables parallelization of environment dynamics across multiple CPU cores to speed up on-policy learning algorithms. The framework integrates with OpenAI Gym and implements multiple RL algorithms including DQN, Double DQN, Dueling DQN, A2C, DDPG, and Prioritized Experience Replay.
Frequently asked
- What is danaugrs/huskarl?
- A TensorFlow 2.0-based deep reinforcement learning framework for modular and fast prototyping of RL algorithms.
- Is huskarl open source?
- Yes — danaugrs/huskarl is open source, released under the MIT license.
- What language is huskarl written in?
- danaugrs/huskarl is primarily written in Python.
- How popular is huskarl?
- danaugrs/huskarl has 413 stars on GitHub.
- Where can I find huskarl?
- danaugrs/huskarl is on GitHub at https://github.com/danaugrs/huskarl.