def reset(self):returnnp.zeros(360) 6. 训练 PPO 代理 创建train.py: from stable_baselines3importPPOfrom robot_envimportRobotEnv env = RobotEnv()model = PPO("MlpPolicy", env, verbose=1)model.learn(total_timesteps=100000...
from stable_baselines3 import PPO import numpy as np from stable_baselines3.common.evaluation import evaluate_policy 下面是具体的代码 # 指定使用的环境 env = gym.make('CartPole-v1') # 指定使用的模型 # 第一个参数指定网络类型,可选MlpPolicy,CnnPolicy,MultiInputPolicy # 如果想使用自定义的网络结构...
obs = env.reset()# while True:# action, _states = model.predict(obs)# obs, rewards, dones, info = env.step(action)# env.render() importgymfromstable_baselines3importPPOfromstable_baselines3.common.env_utilimportmake_vec_env# Parallel environmentsenv = make_vec_env("CartPole-v1", n_en...
我们可以从第 250000 步继续训练该模型,完整代码如下: importgymfromstable_baselines3importPPOimportosmodels_dir='models/PPO'model_path=f'{models_dir}/250000.zip'env=gym.make('LunarLander-v2')env.reset()model=PPO.load(model_path,env=env)episodes=10forepinrange(episodes):obs=env.reset()whileTr...
nn as nn from stable_baselines3 import PPO from stable_baselines3.common.torch_layers import BaseFeaturesExtractor class CustomCNN(BaseFeaturesExtractor): """ :param observation_space: (gym.Space) :param features_dim: (int) Number of features extracted. This corresponds to the number of unit ...
🐛 Bug I have the following problem when setting verbose = 1 or 2 in model and then train, which I believe is a problem with sys.stdout: To Reproduce from stable_baselines3 import PPO model = PPO("MlpPolicy", "CartPole-v1", verbose=1).lea...
importgymnasiumasgymfromstable_baselines3importPPOenv=gym.make("CartPole-v1",render_mode="human")model=PPO("MlpPolicy",env,verbose=1)model.learn(total_timesteps=10_000)vec_env=model.get_env()obs=vec_env.reset()foriinrange(1000):action,_states=model.predict(obs,deterministic=True)obs,reward...
importgymfromstable_baselines3importPPO env = gym.make("CartPole-v1") model = PPO("MlpPolicy", env, verbose=1) model.learn(total_timesteps=10000) obs = env.reset()foriinrange(1000): action, _states = model.predict(obs, deterministic=True) obs, reward, done, info = env.step(action...
import gym from stable_baselines3 import PPO from stable_baselines3.common.callbacks import BaseCallback from stable_baselines3.common.monitor import Monitor class CustomMonitor(Monitor): def __init__(self, env, filename=None, allow_early_resets=True, reset_keywords=(), info_keywords=()): ...
import shap import torch import torch.nn as nn from stable_baselines3 import PPO class sb3Wrapper(nn.Module): def __init__(self, model): super(sb3Wrapper,self).__init__() self.extractor = model.policy.mlp_extractor self.policy_net = model.policy.mlp_extractor.policy_net self.action_net...