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)
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Evaluation results
- mean_reward on PandaReachDense-v3self-reported-0.14 +/- 0.09