importgymfromstable_baselines3importPPOdefmain():env=gym.make('CartPole-v1')# 创建环境model=PPO("MlpPolicy",env,verbose=1)# 创建模型model.learn(total_timesteps=20000)# 训练模型model.save("ppo_cartpole")# 保存模型test_model(model)# 测试模型deftest_model(model):env=gym.make('CartPole-v1'...
Saving video to /home/jyli/Robot/rl-baselines3-zoo/logs/ppo/CartPole-v1_1/videos/best-model-ppo-CartPole-v1-step-0-to-step-1000.mp4 Moviepy - Building video /home/jyli/Robot/rl-baselines3-zoo/logs/ppo/CartPole-v1_1/videos/best-model-ppo-CartPole-v1-step-0-to-step-1000.mp4. Movi...
(3)先共享然后发散:[128, dict(vf=[256], pi=[16])] 更高级的示例 如果您的任务需要对actor/value架构进行更精细的控制,您可以直接重新定义策略: fromtypingimportCallable,Dict,List,Optional,Tuple,Type,Unionimportgymimporttorchasthfromtorchimportnnfromstable_baselines3importPPOfromstable_baselines3.common....
stable_baselines3安装tensorboard 该教程仅适用于初学者,用CPU版本的TensorFlow,安装更快更简单。 如果后续想深入学习机器学习的朋友还是装GPU版本的TensorFlow,一步到位。 文章目录 anaconda官网下载安装 安装TensorFlow PyCharm 安装和配置 安装 配置 anaconda官网下载安装 定位到官网下载页面: anaconda官网下载安装 下载过程...
StableBaselines3环境配置与训练教程要开始使用StableBaselines3进行强化学习,首先需要进行环境配置。你可以选择安装rl-baseline3-zoo,这将提供必要的依赖。如果需要记录训练过程,可以安装相关的视频保存依赖。以PPO算法和经典环境CartPole-v1为例,运行训练后,你会看到类似格式的输出。对于可视化,如果你在...
本文提供StableBaselines3小白教程,重点讲解环境配置与训练流程,旨在简化学习过程。首先,进行环境配置,涉及安装基础依赖如rl-baseline3-zoo,以及可选的log依赖,以确保训练过程记录详尽。接下来,以ppo算法与CartPole-v1环境为例,展示训练实例,目标是获取类似于特定格式的输出结果。考虑到使用远程服务器的...
stable_baselines3 标准化 1. 归一化(Normalization) 将数据集中某一列数值特征的值缩放到0-1区间内: x是指一列的值,x_i是列中的每一个,min(x)是这一列的最小值,max(x)是这一列的最大值。 当要求特征必须是在0-1之间的,此时必须要使用归一化。
import gymnasium as gym import torch as th from stable_baselines3 import PPO # Custom actor (pi) and value function (vf) networks # of two layers of size 32 each with Relu activation function # Note: an extra linear layer will be added on top of the pi and the vf nets, respectively...
Stable-baselines3 how to impose policy action_space different than environment action_spaceAsk Question Asked 27 days ago Modified 27 days ago Viewed 16 times 0 Normally, with eg. sac policy, you would have observations -> sac -> actions -> environment. But because i want t...
classBaseAlgorithm(ABC):""" The base of RL algorithms :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...) :param env: The environment to learn from (if registered in Gym, can be str. Can be None for loading trained models) :param learning_rate: learning rate for the...