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
notebook_login()
!git config --global credential.helper store
package_to_hub(
model=model,
model_name=f"a2c-{env_id}",
model_architecture="A2C",
env_id=env_id,
eval_env=eval_env,
repo_id=f"Ryukijano/a2c-{env_id}", # Change the username
commit_message="Initial commit",
)
import gym
env_id = "PandaReachDense-v2"
# Create the env
env = gym.make(env_id)
# Get the state space and action space
s_size = env.observation_space.shape
a_size = env.action_space
print("_____OBSERVATION SPACE_____ \n")
print("The State Space is: ", s_size)
print("Sample observation", env.observation_space.sample()) # Get a random observation
# 1 - 2
env_id = "PandaReachDense-v2"
env = make_vec_env(env_id, n_envs=100)
# 3
env = VecNormalize(env, norm_obs=True, norm_reward=False, clip_obs=10.)
# 4
model = A2C(policy = "MultiInputPolicy",
env = env,
device = "cuda",
verbose=1)
# 5
model.learn(1_000_000)
# 6
model_name = "a2c-PandaReachDense-v2";
model.save(model_name)
env.save("vec_normalize.pkl")
# 7
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
# Load the saved statistics
eval_env = DummyVecEnv([lambda: gym.make("PandaReachDense-v2")])
eval_env = VecNormalize.load("vec_normalize.pkl", eval_env)
# do not update them at test time
eval_env.training = False
# reward normalization is not needed at test time
eval_env.norm_reward = False
# Load the agent
model = A2C.load(model_name)
mean_reward, std_reward = evaluate_policy(model, eval_env)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
# 8
package_to_hub(
model=model,
model_name=f"a2c-{env_id}",
model_architecture="A2C",
env_id=env_id,
eval_env=eval_env,
repo_id=f"Ryukijano/a2c-{env_id}", # TODO: Change the username
commit_message="Initial commit"
...
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Evaluation results
- mean_reward on PandaReachDense-v2self-reported-1.42 +/- 0.19