rllab-snu/Deep-Reinforcement-Learning
Introduction to Deep Reinforcement Learning
This collection of resources helps you understand and apply deep reinforcement learning algorithms. It provides practical Python-based examples for various techniques, from foundational methods like Q-learning to more advanced actor-critic and inverse reinforcement learning. Researchers, students, or practitioners in AI and machine learning fields can use this to learn and implement DRL.
Use this if you are a student, researcher, or practitioner looking to learn, experiment with, and implement various deep reinforcement learning algorithms from scratch.
Not ideal if you need a production-ready, highly optimized DRL library for large-scale applications or a high-level API for immediate deployment without understanding the underlying mechanics.
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Nov 24, 2025
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