Comments (7)
你这里的obs维度具体是多少?我猜你的问题是:输入高维的obs(应该是同时使用了自己定义的神经网络),导致collect_model.forward
输出的last_value维度是(B, 1)
,从而使得在default_decollate
函数中报错。
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比如输入是(256,9,100)9是agent数量 100是obs空间
经过forward后out[value]是256*[1,9],default_decollate后会变成256 * [9]
而计算last_value的时候输入是(1,9,100),输出是1*[1,9] 没有default_decollate中的降维逻辑,维度就对不上了
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- 哦哦哦MARL的情况啊,可以稍等一周么,下周我们会推出MAPPO版本,现在的PPO实现确实是没考虑MARL的情况。
- 如果你比较着急的话,可以先把
batch size
和agent
维度合并到一起,过这些模块,然后再拆一下。
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把batch size和agent维度合并到一起讲道理也是不行的,这样的话last_value就要取最后agent_num这么多个样本 不知道其他地方还会不会有问题,我现在试了下把default_decollate中的降维逻辑加到last_value上应该就可以了
if len(last_value.shape) > 1:
last_value = last_value.squeeze(0)
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嗯这件事还是得看你是对每个agent一个value还是多个agent使用同一个value,处理就需要case by case了。之后我们会整理一下各种情况,在MAPPO更新的版本中把相应各种情况都处理下,并给出具体的维度相关注释。
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好像要改造的地方确实还不少 大佬加油吧 坐等mappo了
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好的保持对这个issue的持续关注吧,我们之后也会持续更新相关内容
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