nithin04/a2c-PandaReachDense-v3

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
env_id = "PandaReachDense-v3"
env = gym.make(env_id)
s_size = env.observation_space.shape
a_size = env.action_space
env = make_vec_env(env_id, n_envs=4)
env = VecNormalize(venv=env, norm_obs=True, norm_reward=True, clip_obs=10)
model = A2C(policy="MultiInputPolicy", env=env, verbose=1)
model.learn(1_000_000)
model.save("a2c-PandaReachDense-v3")
env.save("vec_normalize.pkl")
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
eval_env = DummyVecEnv([lambda: gym.make("PandaReachDense-v3")])
eval_env = VecNormalize.load("vec_normalize.pkl", eval_env)
eval_env.render_mode = "rgb_array"
eval_env.training = False
eval_env.norm_reward = False
model = A2C.load("a2c-PandaReachDense-v3")
mean_reward, std_reward = evaluate_policy(model, eval_env)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")

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