README.md

metadata

library_name: stable-baselines3
tags:
  - PandaReachDense-v3
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: A2C
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: PandaReachDense-v3
          type: PandaReachDense-v3
        metrics:
          - type: mean_reward
            value: '-0.14 +/- 0.09'
            name: mean_reward
            verified: false

A2C Agent playing PandaReachDense-v3

This is a trained model of a A2C agent playing PandaReachDense-v3 using the stable-baselines3 library.

Usage (with Stable-baselines3)

import os

import gymnasium as gym
import panda_gym

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

Environment

env_id = "PandaReachDense-v3"

# Create the env
env = gym.make(env_id)

Model

model = A2C(policy = "MultiInputPolicy",
            env = env,
            learning_rate = 0.0001,
            n_steps = 10,
            verbose=1)