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目前的问题是在Messenger_env环境交互的时候每一步得到了两个比较重要的信息一个是grid_image, 一个是manual_embeds,但是需要注意的是每次step()后grid_image会变化,但是manual_embeds每一个envs下都是保持不变的。 我期望的是在对grid_image通过编码器进行编码成obs latent的时候能够将manual_embeds进行拼接,但是这部分代码不知道以哪种方式实现比较好

@puyuan1996 puyuan1996 added research Research work in progress environment New or improved environment labels Aug 18, 2025

if use_manual:
self.feature_merge_linear = nn.Linear(self.embedding_dim + manual_dim, self.embedding_dim)

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self.feature_merge_linearh后面应该和原来的obs_embeddings执行相同的norm?

[maunal_sentence], truncation=True, padding="max_length", max_length=self.max_seq_len, return_tensors='pt')
# ts = {k: v.to(self.device) for k, v in ts.items()}
with torch.no_grad():
self.manual_embeds = self.manual_encoder(**tokenized_output).last_hidden_state[:,0,:].squeeze()
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对于messenger,self.manual_embeds目前是使用的固定的手册文段通过bge编码得到的吗

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我目前发现了unizero在kv_cache中的一个bug, 可能导致时间unizero没有正确利用到kvcace,这可能是目前加了manual_embeddings性能没有明显提升的主要原因哈,这个bug我在mspacman上验证修复后你再merge然后重跑一下实验哈。不过就算是单步的,加了manual_embeddings应该相比之前性能会有提升一点才对,可能还有其他问题。

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你可以先实现探索相关的方法哈,等我把unizero的kv_cache bug修复再跑这边的实验

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