README.md

metadata

library_name: stable-baselines3
tags:
  - PandaReachDense-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: A2C
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: PandaReachDense-v2
          type: PandaReachDense-v2
        metrics:
          - type: mean_reward
            value: '-1.39 +/- 0.30'
            name: mean_reward
            verified: false

A2C Agent playing PandaReachDense-v2

This is a trained model of a A2C agent playing PandaReachDense-v2 using the stable-baselines3 library.

The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Usage (with SB3 RL Zoo)

RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo
SB3: https://github.com/DLR-RM/stable-baselines3
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo tqc --env PandaReachDense-v2 -orga sb3 -f logs/
python enjoy.py --algo a2c --env PandaReachDense-v2  -f logs/

Training (with the RL Zoo)

python train.py --algo a2c --env PandaReachDense-v2 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env PandaReachDense-v2 -f logs/ -orga sb3

Panda Gym environments: arxiv.org/abs/2106.13687