Skip to content

Latest commit

 

History

History
41 lines (32 loc) · 1.85 KB

README.md

File metadata and controls

41 lines (32 loc) · 1.85 KB
Raw

Blackjack

Linked Article on Medium : https://medium.com/@ashish.dhiman/optimal-blackjack-strategy-with-reinforcement-learning-867ae03584ec

Step 1 : To run the all the experiments can be run using the run files located in the modules specific to the MDPs

  1. Blackjack
  • blackjack.run_BJ_policy_iteration.py
  • blackjack.run_BJ_value_iteration.py
  • blackjack.run_BJ_q_learning.py

All the above files run the code specific to the algorithm. The hyper-parameters can be changed within the module.

Step 2: Once the algorithms executed, the necessary stats and policy images will be stored in the vi_stats, pi_stats, ql_stats directories for the respective algorithms.

These can now be used to generate various plots using the two include jupyer notebooks as follows :

  • frozen_lake_analysis.ipynb
  • blackjack_analysis.ipynb

Other files

agent.py : Contains the QLearningAgent which can be used for both frozen lake and Blackjack common.py : Some common functions for frozen lake stats and policy generation gym_to_mdp.py : Contains code to convert the Gym transition matrices to MDP toolbox and vice versa policy_iteration.py : Wrapper around MDPToolbox policy iteration which also saves the stats value_iteration.py : Wrapper around MDPToolbox value iteration which also saves the stats q_learning.py : Q-Learning training loop where agent interacts with the environment blackjack.blackjack_util.py : Utility functions for building the Model for Blackjack and simulating the gameplay blackjack.visualize_policy.py : Visualize the policy for the Blackjack game frozen_lake.frozen_lake_game.py : Simulation of Frozen lake game and getting the environment with parametes frozen_lake.visualize_policy.py : Visualize the policy for the FrozenLake game

Folowing python libraries are used only :

  • matplotlib
  • gymnasium
  • mdptoolbox
  • seaborn
  • numpy
  • scipy
  • tqdm
  • pandas