self.action_space = gym.spaces.Discrete(3)self.observation_space = gym.spaces.Box(low=0.0, high=10.0, shape=(360,), dtype=np.float32) def laser_callback(self, msg):self.laser_data = np.array(msg.ranges) def step...
需要说明的是,stable_baselines3的侧重对象应该是初步接触深度强化的萌新玩家和偏强化学习理论研究的同学们,一旦遇到实际工程场景的研究和部署,我个人觉得还是自己搭建网络完成各种utils的搭建比较靠谱,stable_baselines3能够提供的utils不是细粒度的。 当然,直接在stable_baselines3的源代码上修改也是可以的,后面有时间我会...
实在不行,兜底stable-baseline3是肯定没有问题的。 一个框架好不好用,第一观感就是它的quick start和tutorial。 pip install stable-baselines3[extra] 从官网上看,还是stable-baselines3成熟,安装也简单。 stable-baselines3,要求action_space这个可以理解,因为动作空间长度是确定的;但要求observation_space这个比较奇怪...
self).__init__() # 设置环境参数 self.observation_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32) self.action_space = gym.spaces.Discrete(2) # 假设有两个动作 self.state = np.random.uniform(low=-1.0, high=1.0, size=(1,)) def...
__class__.__name__}.load(path, env)` instead" ) # Check that the observation spaces match check_for_correct_spaces(env, self.observation_space, self.action_space) # Update VecNormalize object # otherwise the wrong env may be used, see https://github.com/DLR-RM/stable-baselines3/...
基于Stable-Baselines3 训练 ROS2 机器人自主导航 1. 前言 自主导航是移动机器人领域的核心任务之一,传统路径规划方法(如 Dijkstra、A* 和 DWA)虽然在静态环境中表现良好,但在动态环境中往往难以适应变化。强化学习(Reinforcement Learning, RL)为机器人导航提供了一种新的思路,使机器人可以通过与环境的交互不断优化...
pipinstallstable-baselines3[extra] 从官网上看,还是stable-baselines3成熟,安装也简单。 stable-baselines3,要求action_space这个可以理解,因为动作空间长度是确定的;但要求observation_space这个比较奇怪,每次观察的状态的shape必须一致?在投资里做不到啊,比如A股/美股的投资日历不同,那么有些天就不是所以的标的都可以...
space = spaces.Box(===(N_CHANNELSHEIGHTWIDTH)=np.uint8) (action): observation = .observation_space.sample() reward = done = info = {} observationrewarddoneinfo (): observation = .observation_space.sample() observation (mode=): ():1、继承gym.Env。2、定义动作与...
Fixed check_env for Sequence observation space (@corentinlger) Prevents instantiating BitFlippingEnv with conflicting observation spaces (@kylesayrs) Fixed ResourceWarning when loading and saving models (files were not closed), please note that only path are closed automatically, ...
__init__() # Define action and observation space # They must be gym.spaces objects # Example when using discrete actions: self.action_space = spaces.Discrete(N_DISCRETE_ACTIONS) # Example for using image as input (channel-first; channel-last also works): self.observation_space = spaces....