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请输入英文单字，中文词皆可：

amputate    音标拼音: ['æmpjət,et]
vt. 切断,删除

切断,删除

amputate
v 1: remove surgically; "amputate limbs" [synonym: {amputate}, {cut
off}]

Amputate \Am"pu*tate\, v. t. [imp. & p. p. {Amputated}; p. pr. &
vb. n. {Amputating}.] [L. amputatus, p. p. of amputare: amb-
putare to prune, putus clean, akin to E. pure. See
{Putative}.]
1. To prune or lop off, as branches or tendrils.
[1913 Webster]

2. (Surg.) To cut off (a limb or projecting part of the
body). --Wiseman.
[1913 Webster]


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英文字典中文字典相关资料:


  • GitHub - A-SHOJAEI dreamerv3-robotic-control: DreamerV3 (29. 5M params) for robotic . . .
    A from-scratch PyTorch implementation of DreamerV3 (Hafner et al , 2023), the state-of-the-art model-based reinforcement learning algorithm The agent learns a world model from pixel observations, then trains a policy entirely inside its learned imagination
  • danijar dreamerv3: Mastering Diverse Domains through World Models - GitHub
    This repository contains a reimplementation of DreamerV3 based on the open source DreamerV2 code base It is unrelated to Google or DeepMind The implementation has been tested to reproduce the official results on a range of environments
  • [2301. 04104] Mastering Diverse Domains through World Models
    We present DreamerV3, a general algorithm that outperforms specialized methods across over 150 diverse tasks, with a single configuration Dreamer learns a model of the environment and improves its behavior by imagining future scenarios
  • [2301. 04104] Mastering Diverse Domains through World Models
    This paper presents DreamerV3, a general and scalable reinforcement learning algorithm that masters a wide range of domains with fixed hyperparameters To achieve this, we systematically address varying signal magnitudes and instabilities in all of its components
  • DreamerV3: Robust Latent Model-Based RL
    DreamerV3 is a robust, general-purpose model-based RL algorithm that learns latent world models and leverages imagination-based planning for superior performance
  • EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data - PMLR
    In this paper, we introduce EfficientZero V2, a general framework designed for sample-efficient RL algorithms We have expanded the performance of EfficientZero to multiple domains, encompassing both continuous and discrete actions, as well as visual and low-dimensional inputs
  • DreamerV3: Model-Based RL Algorithm
    DreamerV3 is a model-based reinforcement learning algorithm that learns compact latent world models and optimizes policies via efficient imagined rollouts
  • dreamerv3 · PyPI
    DreamerV3 masters a wide range of domains with a fixed set of hyperparameters, outperforming specialized methods Removing the need for tuning reduces the amount of expert knowledge and computational resources needed to apply reinforcement learning
  • DreamerV3: Mastering Diverse Domains through World Models
    DreamerV3 builds upon DreamerV2 1, but does not significantly change the algorithm Instead, a “bag of tricks” is employed to stabilize and normalize learning across a wide range of environments The DreamerV3 architecture is essentially the same as in DreamerV2 and is pictured in the figure above
  • Mastering diverse control tasks through world models - Nature
    Here we present the third generation of Dreamer, a general algorithm that outperforms specialized methods across over 150 diverse tasks, with a single configuration Dreamer learns a model of





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