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Panda Control RL Suite

Description

The goal of this project is to handle every task in the panda-gym library. This repo can also be used as a starting point for further research without having to set-up the project from scratch.

  • The simulation is based on PyBullet physics engine wrapped in Gymnasium.

  • Environment created using: panda-gym.

  • Baselines obtained with Stablebaselines3.

How to run

First, install dependencies (python==3.8 required)

# clone project   
git clone https://github.com/lambdavi/panda-control-rl.git

# install requirements   
cd panda-control-rl 
pip install -r requirements.txt

Available scripts

# run training script (example: training on PickAndPlace-v3 task)   
python train.py --env_id PandaPickAndPlace-v3 --algo ddpg

# run hyperparameters tuning (example: on PandaReach-v3 with SAC) 
python tuning.py --env_id PandaReach-v3 --algo sac

# eval your agent
python eval.py --env_id PandaReach-v3 --algo ddpg --path models/PandaReach_DDPG_50000_steps.zip

# just visualize the environment (random actions)
python visualize.py --env_id PandaReach-v3

Arguments for train:

  • --lr: Learning rate

    • Type: float
    • Default: 0.001

  • --gamma: Gamma value

    • Type: float
    • Default: 0.99

  • --buffer_size: Buffer size

    • Type: int
    • Default: 1,000,000

  • --batch_size: Batch size

    • Type: int
    • Default: 100

  • --tau: Tau value

    • Type: float
    • Default: 0.005

  • --learning_starts: When to start the learning

    • Type: int
    • Default: 100

  • --steps: Number of steps

    • Type: int
    • Default: 50,000

  • --env_id: Environment ID

    • Type: str
    • Default: "PandaReach-v3"
    • Choices: ["PandaReach-v3", "PandaReachDense-v3", "PandaPickAndPlace-v3", "PandaPickAndPlaceDense-v3"]

  • --algo: Algorithm to solve the task

    • Type: str
    • Default: "ddpg"
    • Choices: ["ddpg", "sac", "dqn"]

Citation

@article{gallouedec2021pandagym,
    title        = {{panda-gym: Open-Source Goal-Conditioned Environments for Robotic Learning}},
    author       = {Gallou{\'e}dec, Quentin and Cazin, Nicolas and Dellandr{\'e}a, Emmanuel and Chen, Liming},
    year         = 2021,
    journal      = {4th Robot Learning Workshop: Self-Supervised and Lifelong Learning at NeurIPS},
    }