feature(yzj): add ptz ctde pipeline #149
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puyuan1996
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Nov 26, 2023
lzero/model/muzero_model_mlp.py
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| next_latent_state, reward = self.dynamics_network(state_action_encoding) | ||
| agent_state_action_encoding = torch.cat((agent_latent_state, action_encoding), dim=1) | ||
| global_state_action_encoding = torch.cat((agent_latent_state, global_latent_state, action_encoding), dim=1) |
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- global_state_action_encoding 有必要把agent_latent_state也拼接进去吗？
- 拼接进去后，action_encoding只占了5/(256*2+5),信息密度是否过低呢
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- s‘，s1‘， s2’， s3'， r =(s,s1,s2,s3,a1,a2,a3) 用一个网络建模联合dynamic function，需要同时考虑team中每个agent的信息。
- collect按照team存储data。
- foward_learn中需要更改数据处理流程。unroll 5步，是整个team同时roll 5步。
- foward_learn 中reward的处理。
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global_state_dynamic的输入只有一个agent action, 没有joint action，是不合理的
lzero/policy/muzero.py
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| policy_logits = policy_logits.detach().cpu().numpy().tolist() | ||
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| legal_actions = [[i for i, x in enumerate(action_mask[j]) if x == 1] for j in range(active_collect_env_num)] | ||
| reward_roots = [[reward_root]*self.cfg.model.agent_num for reward_root in reward_roots] |
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这里reward_roots就是一个长度为24的list，为什么要按照这里的方式变换呢？24=8*3,按理讲，应该每3个对应的reward都是同一个team_reward才对？
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这个地方就是变成每3个智能体用同一个reward去搜索
puyuan1996
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puyuan1996
reviewed
Nov 26, 2023
| @@ -0,0 +1,116 @@ | |||
| from easydict import EasyDict | |||
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mz simple_spread有这个报错，你们那边是正常运行的吗？pettingzoo是1.22.3版吗
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