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: '-1.65 +/- 0.14'
            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.

Usage (with Stable-baselines3)

import pybullet_envs
import panda_gym
import gym

import os

from huggingface_sb3 import load_from_hub, package_to_hub

from stable_baselines3 import A2C
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
from stable_baselines3.common.env_util import make_vec_env

from huggingface_hub import notebook_login

load_model = load_from_hub(
    repo_id="kinkpunk/a2c-PandaReachDense-v2",
    filename="a2c-PandaReachDense-v2.zip",
)

model = A2C.load(load_model)

Panda Gym environments: arxiv.org/abs/2106.13687