q transformer下载 - q transformer源代码下载

q transformer

Ai源码

0.3.0

下载

Q-变压器

Q-Transformer 的实现，通过自回归 Q-Functions 进行可扩展离线强化学习，来自 Google Deepmind

我将保留单个动作 Q 学习的逻辑，只是为了与提议的多个动作自回归 Q 学习进行最终比较。也作为对我自己和公众的教育。

Kotb 等人复制了自回归 Q 学习公式。

安装

$ pip install q-transformer

用法

 import torch

from q_transformer import (
    QRoboticTransformer ,
    QLearner ,
    Agent ,
    ReplayMemoryDataset
)

# the attention model

model = QRoboticTransformer (
    vit = dict (
        num_classes = 1000 ,
        dim_conv_stem = 64 ,
        dim = 64 ,
        dim_head = 64 ,
        depth = ( 2 , 2 , 5 , 2 ),
        window_size = 7 ,
        mbconv_expansion_rate = 4 ,
        mbconv_shrinkage_rate = 0.25 ,
        dropout = 0.1
    ),
    num_actions = 8 ,
    action_bins = 256 ,
    depth = 1 ,
    heads = 8 ,
    dim_head = 64 ,
    cond_drop_prob = 0.2 ,
    dueling = True
)

# you need to supply your own environment, by overriding BaseEnvironment

from q_transformer . mocks import MockEnvironment

env = MockEnvironment (
    state_shape = ( 3 , 6 , 224 , 224 ),
    text_embed_shape = ( 768 ,)
)

# env.init()     should return instructions and initial state: Tuple[str, Tensor[*state_shape]]
# env(actions)   should return rewards, next state, and done flag: Tuple[Tensor[()], Tensor[*state_shape], Tensor[()]]

# agent is a class that allows the q-model to interact with the environment to generate a replay memory dataset for learning

agent = Agent (
    model ,
    environment = env ,
    num_episodes = 1000 ,
    max_num_steps_per_episode = 100 ,
)

agent ()

# Q learning on the replay memory dataset on the model

q_learner = QLearner (
    model ,
    dataset = ReplayMemoryDataset (),
    num_train_steps = 10000 ,
    learning_rate = 3e-4 ,
    batch_size = 4 ,
    grad_accum_every = 16 ,
)

q_learner ()

# after much learning
# your robot should be better at selecting optimal actions

video = torch . randn ( 2 , 3 , 6 , 224 , 224 )

instructions = [
    'bring me that apple sitting on the table' ,
    'please pass the butter'
]

actions = model . get_optimal_actions ( video , instructions )

欣赏

StabilityAI、A16Z 开源 AI 资助计划，以及？感谢慷慨的赞助，以及我的其他赞助商，为我提供了开源当前人工智能研究的独立性

托多

引文

 @inproceedings { qtransformer ,
    title   = { Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions } ,
    authors = { Yevgen Chebotar and Quan Vuong and Alex Irpan and Karol Hausman and Fei Xia and Yao Lu and Aviral Kumar and Tianhe Yu and Alexander Herzog and Karl Pertsch and Keerthana Gopalakrishnan and Julian Ibarz and Ofir Nachum and Sumedh Sontakke and Grecia Salazar and Huong T Tran and Jodilyn Peralta and Clayton Tan and Deeksha Manjunath and Jaspiar Singht and Brianna Zitkovich and Tomas Jackson and Kanishka Rao and Chelsea Finn and Sergey Levine } ,
    booktitle = { 7th Annual Conference on Robot Learning } ,
    year   = { 2023 }
}

 @inproceedings { dao2022flashattention ,
    title   = { Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness } ,
    author  = { Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{'e}, Christopher } ,
    booktitle = { Advances in Neural Information Processing Systems } ,
    year    = { 2022 }
}

 @inproceedings { Kumar2023MaintainingPI ,
    title   = { Maintaining Plasticity in Continual Learning via Regenerative Regularization } ,
    author  = { Saurabh Kumar and Henrik Marklund and Benjamin Van Roy } ,
    year    = { 2023 } ,
    url     = { https://api.semanticscholar.org/CorpusID:261076021 }
}