show_videos('videos', prefix='ppo2') 3. 如何创建自定义环境? 上面知道了一般模型的训练方法和可视化,下面介绍如何创建自定义的 gym 环境。基础的接口应该符合如下规范: import gym from gym import spaces class CustomEnv(gym.Env): """Custom Environment that follows gym interface""" def __init__(se...
我们可以通过添加欧氏距离变量,然后从奖励中减去该距离来实现。 euclidean_dist_to_apple=np.linalg.norm(np.array(self.snake_head)-np.array(self.apple_position))self.total_reward=len(self.snake_position)-3-euclidean_dist_to_apple# default length is 3 创建新的脚本文件snakeenvp4.py,复制上节课中sna...
importgymimporttorchasthfromtorchimportnnfromstable_baselines3.common.torch_layersimportBaseFeaturesExtractorclassCustomCombinedExtractor(BaseFeaturesExtractor):def__init__(self,observation_space:gym.spaces.Dict):# We do not know features-dim here before going over all the items,# so put something dummy...
Any variables exposed in your custom environment will be accessible via locals dict. The example below shows how to access a key in a custom dictionary called my_custom_info_dict in vectorized environments. import numpy as np from stable_baselines3 import SAC from stable_baselines3.common.callbac...
I'm working with a Reinforcement Learning custom environment using Stable Baselines3's SAC algorithm. My environment has a max_steps_per_episode of 500. If the agent doesn't reach the goal within these steps, the episode is truncated and reset. I'm observing an unusual trend...
Provide tuned hyperparameters for each environment and RL algorithm Have fun with the trained agents! Github repo: https://github.com/DLR-RM/rl-baselines3-zoo Documentation: https://stable-baselines3.readthedocs.io/en/master/guide/rl_zoo.html SB3-Contrib: Experimental RL Features We implement ex...
(you are probably using `CnnPolicy` instead of `MlpPolicy`)\n" "If you are using a custom environment,\n" "please check it using our env checker:\n" "https://stable-baselines3.readthedocs.io/en/master/common/env_checker.html" ) n_input_channels = observation_...
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...
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类型的环境,但我们先用Gym中的 ...
Stable Baselines3是一个基于PyTorch的强化学习库，旨在提供清晰、简单且高效的实现。其目的是让研究人员和开发者能轻松地在强化学习项目中使用现代的深度强化学习算法。一小时内掌握Stable Baselines3，通过以下步骤，可获得基本理解及实际应用。学习计划包含：环境配置、基本概念与结构、简单示例运行、代码解析...