Welcome to the Reinforcement Learning Library, an extensive collection of algorithms and practical implementations in the realm of reinforcement learning.
This section features a series of Jupyter Notebooks covering a range of algorithms including:
- A3C (Asynchronous Advantage Actor-Critic)
- Dueling Network Architectures
- DQN (Deep Q-Network)
These notebooks are designed to provide an in-depth understanding of each algorithm with practical examples.
Dive into the Python implementations of core reinforcement learning algorithms:
- DQN (Deep Q-Network)
- DDQN (Double Deep Q-Network)
- N-step DQN
These implementations offer a hands-on experience for those interested in the technical aspects of these algorithms.
Whether you're a student, researcher, or AI enthusiast, this library serves as a valuable resource for exploring and understanding the intricacies of reinforcement learning algorithms.