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Tensorflow Deep Reinforcement Learning agents playing Briscola card game

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deep-briscola

Tensorflow deep reinforcement learning agent playing Briscola card game.

What is Briscola??

This repository contains a Briscola game environment where different agents can play.

  • RandomAgent: choose each move in a random fashion
  • AIAgent: knows the rules and the strategies for winning the game
  • DeepAgent: agent trained using deep reinforcement learning
  • HumanAgent: yourself

Dependencies

sudo apt-get update && sudo apt-get install -y \
  python-dev \
  python3-pip
  
sudo pip install \
  tensorflow \
  hyperopt \
  matplotlib \
  pandas

Alternatively, a Dockerfile with all the dependencies installed is provided in this repo. To use it:

Install Docker on Ubuntu

$ bash docker/build.sh
$ bash docker/run.sh

Usage

Train a model
$ python3 train.py --network dqn --saved_model saved_model_dir
Play against trained deep agent
$ python3 human_vs_ai.py --network dqn --saved_model saved_model_dir
Play against AI Agent
$ python3 human_vs_ai.py

Features

Different networks implemented

Specify the network type using --network command line argument

  • Deep Q Network
  • Deep Recurrent Q Network
  • WIP Synchronous Advantage Actor Critic (A2C)
Self Play

Train multiple agents using the self_train.py python script.

$ python3 self_train.py --network dqn --saved_model saved_model_dir

Results

  • Training a Deep Q Network model for 75k epochs: achieved 85% winrate against a random player.

  • Training a Deep Q Network model for 100k epochs using Self Play: achieved 90% winrate against a random player.

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