fix moe shared weights bug
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@@ -1,6 +1,7 @@
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# 20251230
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# 20251230
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## v0.1.1
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## v0.1.1
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1. 给cts算法加入robogauge异步评估
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1. 给cts算法加入robogauge异步评估
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Fix Bug: 修复MoE中专家使用了共享权重的问题, 换成Conv1D
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# 20251221
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# 20251221
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1. 修改最大地形速度限制, y在所有地形上最大为1.0, z只有平地最大为2.0, x最大为2.0
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1. 修改最大地形速度限制, y在所有地形上最大为1.0, z只有平地最大为2.0, x最大为2.0
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2. 上调难度9地形难度 (都是moe-cts 100k能通过的难度):
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2. 上调难度9地形难度 (都是moe-cts 100k能通过的难度):
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@@ -1,33 +1,13 @@
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# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# -*- coding: utf-8 -*-
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# SPDX-License-Identifier: BSD-3-Clause
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'''
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#
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@File : actor_critic_cts.py
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# Redistribution and use in source and binary forms, with or without
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@Time : 2025/12/30 21:06:08
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# modification, are permitted provided that the following conditions are met:
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@Author : wty-yy
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#
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@Version : 1.0
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# 1. Redistributions of source code must retain the above copyright notice, this
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@Blog : https://wty-yy.github.io/
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# list of conditions and the following disclaimer.
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@Desc : Concurrent Teacher Student Network
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#
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@Refer : CTS https://arxiv.org/abs/2405.10830
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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'''
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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# Copyright (c) 2021 ETH Zurich, Nikita Rudin
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import numpy as np
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import numpy as np
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import torch
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import torch
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@@ -1,33 +1,13 @@
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# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# -*- coding: utf-8 -*-
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# SPDX-License-Identifier: BSD-3-Clause
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'''
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#
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@File : actor_critic_moe_cts.py
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# Redistribution and use in source and binary forms, with or without
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@Time : 2025/12/30 21:06:46
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# modification, are permitted provided that the following conditions are met:
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@Author : wty-yy
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#
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@Version : 1.0
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# 1. Redistributions of source code must retain the above copyright notice, this
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@Blog : https://wty-yy.github.io/
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# list of conditions and the following disclaimer.
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@Desc : Mixture of Experts Concurrent Teacher Student Network
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#
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@Refer : CTS https://arxiv.org/abs/2405.10830, Switch Transformers https://arxiv.org/abs/2101.03961
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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'''
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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# Copyright (c) 2021 ETH Zurich, Nikita Rudin
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import numpy as np
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import numpy as np
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import torch
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import torch
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@@ -208,7 +188,7 @@ class StudentMoEEncoder(nn.Module):
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gating_dim,
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gating_dim,
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hidden_dims=[512, 256],
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hidden_dims=[512, 256],
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expert_num=8,
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expert_num=8,
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expert_hidden_dim=128,
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expert_hidden_dim=256,
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latent_dim=32,
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latent_dim=32,
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activation='elu',
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activation='elu',
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norm_type='l2norm',
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norm_type='l2norm',
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@@ -231,7 +211,12 @@ class StudentMoEEncoder(nn.Module):
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nn.Linear(last_dim, expert_num * expert_hidden_dim),
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nn.Linear(last_dim, expert_num * expert_hidden_dim),
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activation
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activation
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)
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)
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self.experts_out = nn.Linear(expert_hidden_dim, latent_dim)
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self.experts_out = nn.Conv1d(
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in_channels=expert_num*expert_hidden_dim,
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out_channels=expert_num*latent_dim,
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kernel_size=1,
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groups=expert_num
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)
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# Gating network
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# Gating network
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gating_layers = []
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gating_layers = []
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@@ -248,8 +233,9 @@ class StudentMoEEncoder(nn.Module):
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weights = self.gating_network(obs) # (batch, expert_num)
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weights = self.gating_network(obs) # (batch, expert_num)
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shared_features = self.experts_backbone(obs_no_goal)
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shared_features = self.experts_backbone(obs_no_goal)
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expert_hidden = self.experts_hidden(shared_features)
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expert_hidden = self.experts_hidden(shared_features)
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expert_hidden = expert_hidden.view(-1, self.expert_num, expert_hidden.shape[-1] // self.expert_num)
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expert_hidden = expert_hidden.unsqueeze(-1)
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expert_latent = self.experts_out(expert_hidden) # (batch, expert_num, latent_dim)
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expert_latent_flat = self.experts_out(expert_hidden) # (batch, expert_num * latent_dim, 1)
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expert_latent = expert_latent_flat.reshape(-1, self.expert_num, self.latent_dim)
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latent = torch.sum(weights.unsqueeze(-1) * expert_latent, dim=1) # (batch, latent_dim)
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latent = torch.sum(weights.unsqueeze(-1) * expert_latent, dim=1) # (batch, latent_dim)
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latent = self.norm_layer(latent)
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latent = self.norm_layer(latent)
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return latent, weights
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return latent, weights
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