OmniAirl

This is a trustworthy reinforcement learning library only for XJTU IAIR homework, currently maintained by Jiayi Zhou from Xi'an Jiaotong University. Contributions are welcome.

Installation

The following libraries are required for installation:

• numpy
• yaml

To install the dependencies, run:

git clone https://github.com/Gaiejj/omniairl.git
cd omniairl
pip install -r requirements.txt
cd examples
python Q_Learning_K_Bandits.py

Algorithms

Currently, the following reinforcement learning algorithms have been implemented:

• Tabular Q-Learning

Enviornment

• K-Armed Bandit

Result

| Fig1: Epoch Reward | Fig2: Rolling Average Reward (K=5) | |--------------------|-------------------------------------| | | |

| Fig3: Rolling Average Reward (K=10) | Fig4: Rolling Average Reward (K=20) | |----------------------------------------|----------------------------------------| | | |

Contributing

Contributions to this project are welcome! If you find a bug or would like to propose a new feature, please open an issue or submit a pull request.

Contributors

Here's a list of the current contributors to the project:

• Jiayi Zhou

License

This project is licensed under the MIT License - see the LICENSE file for details.