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asuzuki/
a2c-AntBulletEnv-v0

Name: a2c-AntBulletEnv-v0
Availability: InStock

stable-baselines3

AntBulletEnv-v0

deep-reinforcement-learning

reinforcement-learning

model-index

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A2C Agent playing AntBulletEnv-v0

This is a trained model of a A2C agent playing AntBulletEnv-v0 using the stable-baselines3 library.

Usage (with Stable-baselines3)

TODO: Add your code

python

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

#Environment 1: AntBulletEnv-v0
env_id = "AntBulletEnv-v0"
# Create the env
env = gym.make(env_id)

env = make_vec_env(env_id, n_envs=4)

# Adding this wrapper to normalize the observation and the reward
env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10)

#create A2C model
model = A2C(policy = "MlpPolicy",
            env = env,
            gae_lambda = 0.9,
            gamma = 0.99,
            learning_rate = 0.00096,
            max_grad_norm = 0.5,
            n_steps = 8,
            vf_coef = 0.4,
            ent_coef = 0.0,
            seed=11,
            policy_kwargs=dict(
            log_std_init=-2, ortho_init=False),
            normalize_advantage=False,
            use_rms_prop= True,
            use_sde= True,
            verbose=1)

#train agent
model.learn(1_500_000)

# Save the model and VecNormalize statistics when saving the agent
model.save("a2c-AntBulletEnv-v0")
env.save("vec_normalize.pkl")

asuzuki/a2c-AntBulletEnv-v0

A2C Agent playing AntBulletEnv-v0

Usage (with Stable-baselines3)

asuzuki/
a2c-AntBulletEnv-v0