DQN4P2Pmarket

Master thesis' appendix. It includes the environment implemented according to the OpenAI Gym framework and the DQN algorithm implemented using PyTorch. The thesis is available in the MScThesis.pdf document, and chapters 5, 6, 7 have separate directories in the repository.

Structure of the repository

├─── chapter5
│   ├─── env
│   └─── runs
├───chapter6
│   ├─── env
│   ├─── runs
└─── chapter7
    ├─── env
    └─── runs

• env directories contain the environments used in each chapter.
• runs directories contain the results for each each trained.
• train_{}.py scripts contains the DQN algorithm implemented.
• results.ipynb is a jupyternotebook with the results analyses.