This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous agents. It serves as a research-oriented collection of implementations that cover fundamental decision-making strategies, including dynamic programming, temporal difference learning, and policy gradient methods.
The project distinguishes itself by offering specialized frameworks for deep reinforcement learning and structured decision modeling. It includes implementations for deep Q-learning that utilize neural networks, experience replay, and prioritized sampling to approximate action values in complex environments. Additionally, it provides a suite of solvers for Markov decision processes that compute optimal policies and value functions through iterative evaluation and improvement techniques.
The library supports a broad range of learning architectures, enabling the optimization of policies in both discrete and continuous action spaces. It facilitates the study of agent behavior through various estimation methods, such as Monte Carlo sampling and actor-critic architectures, which balance exploration and exploitation during the training process.
The repository is structured as a collection of Jupyter Notebooks, providing documented examples and implementations for testing and researching reinforcement learning algorithms.
