DeepLearning量化交易项目,使用深度学习技术（CNN、RNN、LSTM以及复合神经网络）对价格数据进行预测（全套源码）

## NEAT Agent trained with the NEAT-Python library to play Super Mario World ## Neuroevolution Agent trained in OpenAI's Retro environment with the game 'Super Mario World' for the SNES. This program makes heavy use of [NEAT-Python](https://github.com/codereclaimers/neat-python) and its standard configurations. The in-game data OpenAI's retro environment provides for Super Mario World has been enhanced in the custom `data.json` provided, which should replace the standard `data.json` of super mario world. The ram addresses of this in-game data has been found through the [ROM RAM map of Super mario World](https://www.smwcentral.net/?p=nmap&m=smwram). The following in-game information has been added: * x_pos_player * midway_point_flag * timer_hundreds * timer_tens * timer_ones * level_exit_info --- #### Setup and Execute #### 1. Replace the standard `data.json` in the installation directory of the retro package. Assuming Python 3.7 in a virtualenv: ``` $ cp -f ./data.json ./venv/lib/python3.7/site-packages/retro/data/stable/SuperMarioWorld-Snes/ ``` 2. Configure HyperParameters of `neat-config` as well as those in `evolve-agent.py` 3. Execute `$ python3 evolve-agent.py`