/CS-GY_9223_Deep_Learning

Primary LanguageJupyter Notebook

CS-GY 9223: Deep Learning Project

This project uses a modified version of RLCard for the RL environments. You can find the RLCard fork here

Contents

Implementations

  • DQNAgent.py: DQN agent
  • fully_connected.py: fully-connected feed forward network with ReLU activations
  • memory.py: Memory buffers used during RL

Kuhn Poker

  • kuhn_poker_cfr_train.ipynb: Train CFR agent
  • kuhn_poker_dqn_train.ipynb: Train DQN agent
  • kuhn_poker_dqn_analyze.ipynb: Examine the moves the DQN agent makes
  • kuhn_poker_eval.ipynb: Evaluate the agents against each other

Leduc hold'em

  • leduc_holdem_cfr_train.ipynb: Train CFR agent
  • leduc_holdem_dqn_train.ipynb: Train the DQN agent
  • leduc_holdem_dqn_analyze.ipynb Examine the moves the DQN agent makes
  • leduc_holdem_eval.ipynb: Evaluate the agents against each other

Limit hold'em

  • limit_holdem_eval.ipynb: Evaluate the agents against each other
  • limit_holdem_dqn_train.ipynb: Train the DQN agent
  • limit_holdem_human_dqn.py: Play against the DQN agent

Other

  • vis.ipynb: Visualizations for the report
  • images/: Folder containing images of visualizations for the report
  • spec-file.txt: List of Conda dependencies for this project's environment

Results

The above notebooks and scripts reference files in the models and experiments folders. You can find these folders on Google Drive.