from stable_baselines3 import PPO, A2C # DQN coming soon from stable_baselines3.common.env_util import make_vec_env # 构建环境 env = GoLeftEnv(grid_size=10) env = make_vec_env(lambda: env, n_envs=1) 训练智能体 # 训练智能体 model = A2C('MlpPolicy', env, verbose=1).learn(5000)...
不用担心,stable_baselines3做了很好的封装,即便你对DQN的原理完全不知道,stable_baselines3也能做到让你开箱即用: env_name="LunarLander-v2"env=gym.make(env_name)env=DummyVecEnv([lambda:env])model=DQN("MlpPolicy",env=env,verbose=1) 上述代码中,头两行我们创建了一个gym环境。第三行的代码则使用了...
from stable_baselines3 import PPO from stable_baselines3.common.env_util import make_vec_env import random import argparse import GymEnv import os import Params def fixed_seed(i): random.seed(i) os.environ['PYTHONHASHSEED'] = str(i) # 为了禁止hash随机化,使得实验可复现 np.random.seed(i)...
stable-baselines dqn stablebaseline3 Mofasa E 49 asked Apr 12 at 3:33 0 votes 0 answers 37 views Multiprocess environement with stablebaseline3 SubprocVecEnv I have a working (complex) Gymnasium environment that needs two processes to work properly, and I want to train an agent to acc...
from toyEnv_mask import PortfolioEnv # file with my custom gym environment from sb3_contrib import MaskablePPO from sb3_contrib.common.wrappers import ActionMasker from stable_baselines3.common.vec_env import DummyVecEnv from stable_baselines3.common.env_util import make_vec_env from...
baselines3版本是1.1.0。使用pip安装更高版本的stable_baselines3可以解决这个问题。我以前 ...
Sync with Stable-Baselines 4年前 .dockerignore Add dockerfile + update doc 4年前 .gitignore Allow env_kwargs in make_vec_env when env ID string supplied (#189) 4年前 .gitlab-ci.yml Reformat with new black version (#408) 3年前 ...
from stable_baselines3.common.vec_env import DummyVecEnv from stable_baselines3.common.evaluation import evaluate_policy 1. 2. 3. 4. 5. 6. 7. DummyVecRnv 用于将 evaluate_policy 使我们更容易测试环境是如何表现的 2、Environment 使用OpenAIGym,如果是自定义的环境,后面会讲如何将自定义环境转化为Gym...
stable_baselines3 a2c common sb2_compat vec_env __init__.py atari_wrappers.py base_class.py bit_flipping_env.py buffers.py callbacks.py cmd_util.py distributions.py env_checker.py env_util.py evaluation.py identity_env.py logger.py monitor.py noise.py off_policy...
import gym from stable_baselines3 import PPO env = gym.make("CartPole-v1") model = PPO("MlpPolicy", env, verbose=1) model.learn(total_timesteps=10_000) obs = env.reset() for i in range(1000): action, _states = model.predict(obs, deterministic=True) obs, reward, done, info = ...