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https://github.com/DLR-RM/rl-baselines3-zoo ## Quick example Most of the library tries to follow a sklearn-like syntax for the Reinforcement Learning algorithms using Gym. Here is a quick example of how to train and run PPO on a cartpole environment: ...
不过stable_baselines3能做的不只这些。 作为一个在GitHub上有2k star的深度强化学习的工具包,stable_baselines3能够快速完成强化学习算法的搭建训练和评估,包括保存,录视频等等你需要在进行深度强化实验方面要用到的函数和方法,基本都有。stable_baselines3的文档如下: 正片开始 环境的安装和启动 今天我们要训练的openai...
git clone https://github.com/DLR-RM/rl-baselines3-zoo.git cd rl-baselines3-zoo pip install -e . 装下log的依赖(可选) pip install tensorboard wandb CartPole-v1训练示例 以ppo算法和CartPole-v1环境为例 env=CartPole-v1 alog=ppo python rl_zoo3/train.py \ --algo $alog \ --env $env ...
Stable Baselines3 (SB3)是 PyTorch 中强化学习算法的一组可靠实现。它是Stable Baselines的下一个主要版本。 Github 仓库:https ://github.com/DLR-RM/stable-baselines3 论文:https ://jmlr.org/papers/volume22/20-1364/20-1364.pdf RL Baselines3 Zoo(SB3 的训练框架):https ://github.com/DLR-RM/rl-...
.github Add timeout handling for on-policy algorithms (#658) 3年前 docs Add DriverGym project to SB3 project documentation (#665) 3年前 scripts Update doc: SB3-Contrib (#267) 4年前 stable_baselines3 Add timeout handling for on-policy algorithms (#658) ...
git config --global user.name userName git config --global user.email userEmail 分支7 标签14 Antonin RAFFINReformat with new black version (#408)6f822b93年前 524 次提交 提交 .github Add custom objects support + bug fix (#336) 4年前 ...
I´m trying to use this code from a repo in GitHub (https://github.com/nicknochnack/Reinforcement-Learning-for-Trading-Custom-Signals/blob/main/Custom%20Signals.ipynb) in Point 3: model = A2C('MlpLstmPolicy', env, verbose=1) model.learn(total_timesteps=1000000) I got a lot of pro...
I copied the code and was successful in getting it to run in a HuggingFace google colab notebook https://colab.research.google.com/github/huggingface/deep-rl-class/blob/master/notebooks/unit1/unit1.ipynb so I am extremely confused why it does not work on my local machine...
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...