VinF/deer
DEEp Reinforcement learning framework
What it solves
DeeR is a modular Python library designed to simplify the implementation of Deep Reinforcement Learning (DRL) algorithms, allowing developers to adapt the framework to various needs and environments.
How it works
It provides a set of pre-built, modular components that can be implemented across different environments, including those using OpenAI Gym. The library includes several core DRL techniques such as Double Q-learning, prioritized Experience Replay, and Deep Deterministic Policy Gradient (DDPG).
Who it’s for
It is intended for developers and researchers working with Deep Reinforcement Learning who need a flexible, modular framework to build and test agents.
Highlights
- Modular design for easy adaptation
- Support for Double Q-learning and DDPG
- Prioritized Experience Replay
- Combined Reinforcement via Abstract Representations (CRAR)
- Compatible with OpenAI Gym environments
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