https://github.com/ps48/doodle-jump-rl

Game Bot Reinforcement Learning and Adversarial Attack
https://github.com/ps48/doodle-jump-rl

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Game Bot Reinforcement Learning and Adversarial Attack

• Host: GitHub
• URL: https://github.com/ps48/doodle-jump-rl
• Owner: ps48
• Created: 2021-01-30T19:22:56.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-05-04T06:04:57.000Z (over 3 years ago)
• Last Synced: 2023-03-02T03:05:48.290Z (over 1 year ago)
• Language: Python
• Size: 7.41 MB
• Stars: 0
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Doodle-Jump-RL

## **USC - CSCI 527** Under Prof. Micheal Zyda

Game Bot Reinforcement Learning and Adversarial Attack 

## [Jump to our webpage](https://pranavmswamy.github.io/doodle-jump-website/)



## Getting Started

1. Clone the repository

2. Install the requirements `pip install -r requirements.txt`

## Usage

* Train/Test **DQN & DRQN** Model

```

python deepQAgent.py

  -h, --help            show this help message and exit

  --macos               select model to train the agent

  --human               playing the game manually without agent

  --test                playing the game with a trained agent

  -d {EASY,MEDIUM,HARD}, --difficulty {EASY,MEDIUM,HARD}

                        select difficulty of the game

  -m {dqn,drqn,resnet,mobilenet,mnasnet}, --model {dqn,drqn,resnet,mobilenet,mnasnet}

                        select model to train the agent

  -p MODEL_PATH, --model_path MODEL_PATH

                        path to weights of an earlier trained model

  -lr LEARNING_RATE, --learning_rate LEARNING_RATE

                        set learning rate for training the model

  -g GAMMA, --gamma GAMMA

                        set discount factor for q learning

  --max_memory MAX_MEMORY

                        Buffer memory size for long training

  --store_frames        store frames encountered during game play by agent

  --batch_size BATCH_SIZE

                        Batch size for long training

  --reward_type {1,2,3,4,5,6}

                        types of rewards formulation

  --exploration EXPLORATION

                        number of games to explore

  --channels CHANNELS   set the image channels for preprocessing

  --height HEIGHT       set the image height post resize

  --width WIDTH         set the image width post resize

  --server              when training on server add this flag

  --seed SEED           change seed value for creating game randomness

  --max_games MAX_GAMES

                        set the max number of games to be played by the agent

  --explore {epsilon_g,epsilon_g_decay_exp,epsilon_g_decay_exp_cur}

                        select the exploration vs exploitation tradeoff

  --decay_factor DECAY_FACTOR

                        set the decay factor for exploration

  --epsilon EPSILON     set the epsilon value for exploration

  --attack              use fast fgsm attack to manipulate the input state

  --attack_eps ATTACK_EPS

                        epsilon value for the fgsm attack

```

* Train/Test **A2C** Model

```

python a2cAgent.py --options

```

* Train/Test **PPO** Model

```

python ppoAgent.py --options

```

## Folder Tree

```

Root

|   .gitignore

|   a2cAgent.py

|   deepQAgent.py

|   helper.py

|   ppoAgent.py

|   README.md

|   requirements.txt

|   rewards.py

|

+---game

|   |   doodlejump.py

|   |   LICENSE

|   |   __init__.py

|   |

|   \---assets

|

\---model

    |   a2cTrainer.py

    |   deepQTrainer.py

    |   networks.py

    |   ppoTrainernew.py

```