v0.1.5 prev1; Add DualMoE

UPDATE.md

@@ -1,6 +1,7 @@
 # 20260106
 ## v0.1.5
 1. 加入`go2_ac_moe_cts`, 参考MoELoco将MoE加载Actor-Critic上, 使用非共享权重和全goal输入
+2. 加入`go2_dual_moe_cts`, student和actor都使用MoE结构, 使用非共享权重和全goal输入
 # 20260105
 ## v0.1.4
 1. `legged_gym/utils/terrain.py`加入地形难度选择默认`IS_HARD=True`

legged_gym/envs/__init__.py

@@ -1,7 +1,7 @@
 from legged_gym import LEGGED_GYM_ROOT_DIR, LEGGED_GYM_ENVS_DIR
 
 from legged_gym.envs.go2.go2_env import Go2Robot
-from legged_gym.envs.go2.go2_config import GO2Cfg, GO2CfgPPO, GO2CfgCTS, GO2CfgMoECTS, GO2CfgMCPCTS, GO2CfgACMoECTS
+from legged_gym.envs.go2.go2_config import GO2Cfg, GO2CfgPPO, GO2CfgCTS, GO2CfgMoECTS, GO2CfgMCPCTS, GO2CfgACMoECTS, GO2CfgDualMoECTS
 from .base.legged_robot import LeggedRobot
 
 from legged_gym.utils.task_registry import task_registry
@@ -11,3 +11,4 @@ task_registry.register("go2_cts", Go2Robot, GO2Cfg(), GO2CfgCTS())
 task_registry.register("go2_moe_cts", Go2Robot, GO2Cfg(), GO2CfgMoECTS())
 task_registry.register("go2_mcp_cts", Go2Robot, GO2Cfg(), GO2CfgMCPCTS())
 task_registry.register("go2_ac_moe_cts", Go2Robot, GO2Cfg(), GO2CfgACMoECTS())
+task_registry.register("go2_dual_moe_cts", Go2Robot, GO2Cfg(), GO2CfgDualMoECTS())

legged_gym/envs/base/legged_robot_config.py

@@ -376,3 +376,12 @@ class LeggedRobotCfgACMoECTS(LeggedRobotCfgCTS):
     class runner(LeggedRobotCfgCTS.runner):
         policy_class_name = 'ActorCriticACMoECTS'
         algorithm_class_name = 'ACMoECTS'
+
+class LeggedRobotCfgDualMoECTS(LeggedRobotCfgCTS):
+    class policy(LeggedRobotCfgCTS.policy):
+        expert_num = 8 # number of experts in the student model
+        student_encoder_hidden_dims = [512, 256, 128]
+
+    class runner(LeggedRobotCfgCTS.runner):
+        policy_class_name = 'ActorCriticDualMoECTS'
+        algorithm_class_name = 'DualMoECTS'

legged_gym/envs/go2/go2_config.py

@@ -1,5 +1,5 @@
 import math
-from legged_gym.envs.base.legged_robot_config import LeggedRobotCfg, LeggedRobotCfgPPO, LeggedRobotCfgCTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMCPCTS, LeggedRobotCfgACMoECTS
+from legged_gym.envs.base.legged_robot_config import LeggedRobotCfg, LeggedRobotCfgPPO, LeggedRobotCfgCTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMCPCTS, LeggedRobotCfgACMoECTS, LeggedRobotCfgDualMoECTS
 
 class GO2Cfg(LeggedRobotCfg):
     class init_state(LeggedRobotCfg.init_state):
@@ -285,3 +285,13 @@ class GO2CfgACMoECTS(LeggedRobotCfgACMoECTS):
         experiment_name = 'go2_ac_moe_cts'
         max_iterations = 150000
         save_interval = 500
+
+class GO2CfgDualMoECTS(LeggedRobotCfgDualMoECTS):
+    class policy(LeggedRobotCfgDualMoECTS.policy):
+        expert_num = 8  # number of experts in the student model
+    
+    class runner(LeggedRobotCfgDualMoECTS.runner):
+        run_name = ''
+        experiment_name = 'go2_dual_moe_cts'
+        max_iterations = 150000
+        save_interval = 500

legged_gym/utils/exporter.py

@@ -77,7 +77,8 @@ class _TorchPolicyExporter(torch.nn.Module):
             self.forward = self.forward_cts
         if hasattr(policy, "student_moe_encoder"):
             self.student_moe_encoder = copy.deepcopy(policy.student_moe_encoder).cpu()
-            self.obs_no_goal_mask = copy.deepcopy(policy.obs_no_goal_mask).cpu()
+            if hasattr(policy, "obs_no_goal_mask"):
+                self.obs_no_goal_mask = copy.deepcopy(policy.obs_no_goal_mask).cpu()
             self.history_length = policy.history.shape[1]
             self.history = torch.zeros([1, policy.history.shape[1], policy.history.shape[2]], device='cpu')
             self.forward = self.forward_moe_cts
@@ -98,6 +99,8 @@ class _TorchPolicyExporter(torch.nn.Module):
                 self.rnn = copy.deepcopy(policy.memory_s.rnn)
         else:
             raise ValueError("Policy does not have an actor/student module.")
+        if hasattr(policy, "student_moe_encoder") and hasattr(policy, "actor_moe"):
+            self.forward = self.forward_dual_moe_cts
         # set up recurrent network
         if self.is_recurrent:
             self.rnn.cpu()
@@ -155,6 +158,14 @@ class _TorchPolicyExporter(torch.nn.Module):
         mean, weights = self.actor(x)
         return mean, (weights, latent)
 
+    def forward_dual_moe_cts(self, x):  # x is single observations
+        x = self.normalizer(x)
+        self.history = torch.cat([self.history[:, 1:], x.unsqueeze(1)], dim=1)
+        latent, student_weights = self.student_moe_encoder(self.history.flatten(1))
+        x = torch.cat([latent, x], dim=1)
+        mean, actor_weights = self.actor(x)
+        return mean, (student_weights, actor_weights, latent)
+
     @torch.jit.export
     def reset(self):
         if hasattr(self, 'history'):

rsl_rl/rsl_rl/algorithms/__init__.py

@@ -33,3 +33,4 @@ from .cts import CTS
 from .moe_cts import MoECTS
 from .mcp_cts import MCPCTS
 from .ac_moe_cts import ACMoECTS
+from .dual_moe_cts import DualMoECTS

rsl_rl/rsl_rl/algorithms/dual_moe_cts.py

@@ -0,0 +1,287 @@
+# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+# 
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# Copyright (c) 2021 ETH Zurich, Nikita Rudin
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+import itertools
+from rsl_rl.modules import ActorCriticDualMoECTS
+from rsl_rl.storage import RolloutStorageCTS
+from rsl_rl.algorithms.cts import CTS
+
+class DualMoECTS(CTS):
+    model: ActorCriticDualMoECTS
+    def __init__(self,
+                model,
+                num_envs,
+                history_length,
+                num_learning_epochs=1,
+                num_mini_batches=1,
+                clip_param=0.2,
+                gamma=0.998,
+                lam=0.95,
+                value_loss_coef=1.0,
+                entropy_coef=0.0,
+                load_balance_coef=0.01,
+                learning_rate=1e-3,
+                student_encoder_learning_rate=1e-3,
+                max_grad_norm=1.0,
+                use_clipped_value_loss=True,
+                schedule="fixed",
+                desired_kl=0.01,
+                teacher_env_ratio=0.75,
+                device='cpu',
+                ):
+
+        self.device = device
+
+        self.desired_kl = desired_kl
+        self.schedule = schedule
+        self.learning_rate = learning_rate
+        self.history_length = history_length
+
+        # CTS components
+        self.model = model
+        self.model.to(self.device)
+        self.storage = None # initialized later
+        params1 = [
+            {"params": self.model.teacher_encoder.parameters()},
+            {"params": self.model.critic_experts.parameters()},
+            {"params": self.model.actor_moe.parameters()},
+            {"params": self.model.std}
+        ]
+        self.optimizer1 = optim.Adam(params1, lr=learning_rate)
+        self.optimizer2 = optim.Adam(self.model.student_moe_encoder.parameters(), lr=student_encoder_learning_rate)
+        self.transition = RolloutStorageCTS.Transition()
+
+        # CTS parameters
+        self.clip_param = clip_param
+        self.num_learning_epochs = num_learning_epochs
+        self.num_mini_batches = num_mini_batches
+        self.value_loss_coef = value_loss_coef
+        self.entropy_coef = entropy_coef
+        self.load_balance_coef = load_balance_coef
+        self.gamma = gamma
+        self.lam = lam
+        self.max_grad_norm = max_grad_norm
+        self.use_clipped_value_loss = use_clipped_value_loss
+        self.teacher_num_envs = max(int(num_envs * teacher_env_ratio), 1)
+        self.student_num_envs = num_envs - self.teacher_num_envs
+        student_env_ratio = 1 - teacher_env_ratio
+        self.teacher_env_idxs = torch.tensor([i for i in range(num_envs) if i % int(1/student_env_ratio) != 0], device=self.device)
+        self.student_env_idxs = torch.tensor([i for i in range(num_envs) if i % int(1/student_env_ratio) == 0], device=self.device)
+        assert len(self.teacher_env_idxs) == self.teacher_num_envs, f"{len(self.teacher_env_idxs)=} != {self.teacher_num_envs=}"
+        assert len(self.student_env_idxs) == self.student_num_envs, f"{len(self.student_env_idxs)=} != {self.student_num_envs=}"
+
+    def act(self, obs, privileged_obs, history):
+        history = history.clone()
+        def get_results(obs, privileged_obs, history, is_teacher):
+            actions = self.model.act(obs, privileged_obs, history, is_teacher).detach()
+            return (
+                actions,
+                self.model.evaluate(obs, privileged_obs, history, is_teacher)[0].detach(),
+                self.model.get_actions_log_prob(actions).detach(),
+                self.model.action_mean.detach(),
+                self.model.action_std.detach(),
+            )
+        ti, si = self.teacher_env_idxs, self.student_env_idxs
+        teacher_results = get_results(obs[ti], privileged_obs[ti], history[ti], True)
+        student_results = get_results(obs[si], privileged_obs[si], history[si], False)
+        results = []
+        for x1, x2 in zip(teacher_results, student_results):
+            results.append(torch.cat([x1, x2], dim=0))
+        # Compute the actions and values
+        self.transition.actions = results[0]
+        self.transition.values = results[1]
+        self.transition.actions_log_prob = results[2]
+        self.transition.action_mean = results[3]
+        self.transition.action_sigma = results[4]
+        # need to record obs and critic_obs before env.step()
+        self.transition.history = torch.cat([history[ti], history[si]], dim=0)
+        self.transition.observations = torch.cat([obs[ti], obs[si]], dim=0)
+        self.transition.critic_observations = torch.cat([privileged_obs[ti], privileged_obs[si]], dim=0)
+        real_actions = torch.zeros_like(self.transition.actions)
+        real_actions[ti] = self.transition.actions[:self.teacher_num_envs]
+        real_actions[si] = self.transition.actions[self.teacher_num_envs:]
+        return real_actions
+
+    def compute_returns(self, last_obs, last_privileged_obs, last_history):
+        ti, si = self.teacher_env_idxs, self.student_env_idxs
+        last_values = torch.cat([
+            self.model.evaluate(last_obs[ti], last_privileged_obs[ti], last_history[ti], True)[0].detach(),
+            self.model.evaluate(last_obs[si], last_privileged_obs[si], last_history[si], False)[0].detach(),
+        ], dim=0)
+        self.storage.compute_returns(last_values, self.gamma, self.lam)
+
+    def update(self):
+        mean_value_loss = 0
+        mean_surrogate_loss = 0
+        mean_entropy_loss = 0
+        mean_latent_loss = 0
+        mean_student_load_balance_loss = 0
+        mean_actor_load_balance_loss = 0
+        assert not self.model.is_recurrent
+        data = list(self.storage.mini_batch_generator(self.num_mini_batches, self.num_learning_epochs))
+        teacher_samples = self.teacher_num_envs * self.storage.num_transitions_per_env // self.num_mini_batches
+        student_samples = self.student_num_envs * self.storage.num_transitions_per_env // self.num_mini_batches
+        for sample in data:
+            (
+                obs_batch, privileged_obs_batch, actions_batch, history_batch,
+                target_values_batch, advantages_batch, returns_batch,
+                old_actions_log_prob_batch, old_mu_batch, old_sigma_batch,
+                hid_states_batch, masks_batch
+            ) = sample
+            def get_results(start, end, is_teacher):
+                self.model.act(obs_batch[start:end], privileged_obs_batch[start:end], history_batch[start:end], is_teacher)
+                actions_log_prob = self.model.get_actions_log_prob(actions_batch[start:end])
+                value, weights = self.model.evaluate(obs_batch[start:end], privileged_obs_batch[start:end], history_batch[start:end], is_teacher)
+                mu = self.model.action_mean
+                sigma = self.model.action_std
+                entropy = self.model.entropy
+                return actions_log_prob, value, mu, sigma, entropy, weights
+            teacher_results = get_results(0, teacher_samples, True)
+            student_results = get_results(teacher_samples, teacher_samples + student_samples, False)
+            results = []
+            for x1, x2 in zip(teacher_results, student_results):
+                results.append(torch.cat([x1, x2], dim=0))
+            actions_log_prob_batch = results[0]
+            value_batch = results[1]
+            mu_batch = results[2]
+            sigma_batch = results[3]
+            entropy_batch = results[4]
+            ac_weights = results[5]
+
+            # KL
+            if self.desired_kl != None and self.schedule == 'adaptive':
+                with torch.inference_mode():
+                    kl = torch.sum(
+                        torch.log(
+                            sigma_batch / old_sigma_batch + 1.e-5) + (
+                                torch.square(old_sigma_batch) +
+                                torch.square(old_mu_batch - mu_batch)
+                            ) / (2.0 * torch.square(sigma_batch)) - 0.5, axis=-1)
+                    kl_mean = torch.mean(kl)
+
+                    if kl_mean > self.desired_kl * 2.0:
+                        self.learning_rate = max(1e-5, self.learning_rate / 1.5)
+                    elif kl_mean < self.desired_kl / 2.0 and kl_mean > 0.0:
+                        self.learning_rate = min(1e-2, self.learning_rate * 1.5)
+                    
+                    for param_group in self.optimizer1.param_groups:
+                        param_group['lr'] = self.learning_rate
+
+
+            # Surrogate loss
+            ratio = torch.exp(actions_log_prob_batch - torch.squeeze(old_actions_log_prob_batch))
+            surrogate = -torch.squeeze(advantages_batch) * ratio
+            surrogate_clipped = -torch.squeeze(advantages_batch) * torch.clamp(ratio, 1.0 - self.clip_param,
+                                                                            1.0 + self.clip_param)
+            surrogate_losses = torch.max(surrogate, surrogate_clipped)
+            teacher_surrogate_loss = surrogate_losses[:teacher_samples].mean()
+            student_surrogate_loss = surrogate_losses[teacher_samples:].mean()
+            surrogate_loss = teacher_surrogate_loss + student_surrogate_loss
+            # surrogate_loss = teacher_surrogate_loss
+
+            # Value function loss
+            if self.use_clipped_value_loss:
+                value_clipped = target_values_batch + (value_batch - target_values_batch).clamp(-self.clip_param,
+                                                                                                self.clip_param)
+                value_losses = (value_batch - returns_batch).pow(2)
+                value_losses_clipped = (value_clipped - returns_batch).pow(2)
+                value_loss = torch.max(value_losses, value_losses_clipped).mean()
+            else:
+                value_loss = (returns_batch - value_batch).pow(2).mean()
+            # teacher_value_loss = value_losses[:teacher_samples].mean()
+            # student_value_loss = value_losses[teacher_samples:].mean()
+            # value_loss = teacher_value_loss  # + student_value_loss
+
+            # Load balance loss
+            mean_usage = torch.mean(ac_weights, dim=0)
+            target_usage = torch.full_like(mean_usage, 1.0 / ac_weights.shape[1])
+            actor_load_balance_loss = torch.mean((mean_usage - target_usage).pow(2))
+
+            loss = (
+                surrogate_loss +
+                self.value_loss_coef * value_loss -
+                self.entropy_coef * entropy_batch.mean() +
+                self.load_balance_coef * actor_load_balance_loss
+            )
+
+            # Gradient step
+            self.optimizer1.zero_grad()
+            loss.backward()
+            params_to_clip = itertools.chain.from_iterable(g['params'] for g in self.optimizer1.param_groups)
+            nn.utils.clip_grad_norm_(params_to_clip, self.max_grad_norm)
+            self.optimizer1.step()
+
+            mean_value_loss += value_loss.item()
+            mean_surrogate_loss += surrogate_loss.item()
+            mean_entropy_loss += entropy_batch.mean().item()
+            mean_actor_load_balance_loss += actor_load_balance_loss.item()
+        
+        for sample in data:
+            (
+                obs_batch, privileged_obs_batch, actions_batch, history_batch,
+                target_values_batch, advantages_batch, returns_batch,
+                old_actions_log_prob_batch, old_mu_batch, old_sigma_batch,
+                hid_states_batch, masks_batch
+            ) = sample
+            # Student encoder update
+            student_latent, gating_weights = self.model.student_moe_encoder(history_batch[teacher_samples:])
+            with torch.no_grad():
+                teacher_latent = self.model.teacher_encoder(privileged_obs_batch[teacher_samples:])
+            latent_loss = (teacher_latent - student_latent).pow(2).mean()
+
+            # Load balance loss
+            mean_usage = torch.mean(gating_weights, dim=0)
+            target_usage = torch.full_like(mean_usage, 1.0 / gating_weights.shape[1])
+            student_load_balance_loss = torch.mean((mean_usage - target_usage).pow(2))
+
+            student_loss = latent_loss + self.load_balance_coef * student_load_balance_loss
+
+            self.optimizer2.zero_grad()
+            student_loss.backward()
+            nn.utils.clip_grad_norm_(self.model.student_moe_encoder.parameters(), self.max_grad_norm)
+            self.optimizer2.step()
+
+            mean_latent_loss += latent_loss.item()
+            mean_student_load_balance_loss += student_load_balance_loss.item()
+
+        num_updates = self.num_learning_epochs * self.num_mini_batches
+        mean_value_loss /= num_updates
+        mean_surrogate_loss /= num_updates
+        mean_entropy_loss /= num_updates
+        mean_latent_loss /= num_updates
+        mean_student_load_balance_loss /= num_updates
+        mean_actor_load_balance_loss /= num_updates
+        self.storage.clear()
+
+        return mean_value_loss, mean_surrogate_loss, mean_entropy_loss, mean_latent_loss, mean_student_load_balance_loss, mean_actor_load_balance_loss

rsl_rl/rsl_rl/modules/__init__.py

@@ -34,3 +34,4 @@ from .actor_critic_cts import ActorCriticCTS
 from .actor_critic_moe_cts import ActorCriticMoECTS
 from .actor_critic_mcp_cts import ActorCriticMCPCTS
 from .actor_critic_ac_moe_cts import ActorCriticACMoECTS
+from .actor_critic_dual_moe_cts import ActorCriticDualMoECTS

rsl_rl/rsl_rl/modules/actor_critic_dual_moe_cts.py

@@ -0,0 +1,148 @@
+# -*- coding: utf-8 -*-
+'''
+@File    : actor_critic_moe_cts.py
+@Time    : 2025/12/30 21:06:46
+@Author  : wty-yy
+@Version : 1.0
+@Blog    : https://wty-yy.github.io/
+@Desc    : Multiplicative Compositional Policies Concurrent Teacher Student Network
+@Refer   :  CTS https://arxiv.org/abs/2405.10830,
+            Switch Transformers (Load Balance) https://arxiv.org/abs/2101.03961
+            MoE-Loco (AC MoE) http://arxiv.org/abs/2503.08564
+'''
+import numpy as np
+
+import torch
+import torch.nn as nn
+from torch.distributions import Normal
+from rsl_rl.modules.utils import MLP, MoE, Experts, L2Norm, SimNorm
+
+class ActorCriticDualMoECTS(nn.Module):
+    is_recurrent = False
+    def __init__(self,  num_obs,
+                        num_critic_obs,
+                        num_actions,
+                        num_envs,
+                        history_length,
+                        actor_hidden_dims=[512, 256, 128],
+                        critic_hidden_dims=[512, 256, 128],
+                        teacher_encoder_hidden_dims=[512, 256],
+                        student_encoder_hidden_dims=[512, 256, 128],  # last dim is expert hidden dim
+                        expert_num=8,
+                        activation='elu',
+                        init_noise_std=1.0,
+                        latent_dim=32,
+                        norm_type='l2norm',
+                        **kwargs):
+        if kwargs:
+            print("ActorCritic.__init__ got unexpected arguments, which will be ignored: " + str([key for key in kwargs.keys()]))
+        assert norm_type in ['l2norm', 'simnorm'], f"Normalization type {norm_type} not supported!"
+        super().__init__()
+        self.num_actions = num_actions
+        self.history_length = history_length
+
+        mlp_input_dim_t = num_critic_obs
+        mlp_input_dim_s = num_obs * history_length
+        mlp_input_dim_c = latent_dim + num_critic_obs
+        mlp_input_dim_a = latent_dim + num_obs
+
+        # History
+        self.register_buffer("history", torch.zeros((num_envs, history_length, num_obs)), persistent=False)
+
+        # Teacher encoder
+        self.teacher_encoder = nn.Sequential(
+            MLP([mlp_input_dim_t, *teacher_encoder_hidden_dims, latent_dim], activation),
+            L2Norm() if norm_type == 'l2norm' else SimNorm()
+        )
+
+        # Student encoder
+        self.student_moe_encoder = MoE(
+            expert_num=expert_num,
+            input_dim=mlp_input_dim_s,
+            hidden_dims=student_encoder_hidden_dims,
+            output_dim=latent_dim,
+            activation=activation,
+        )
+
+        # MCP Actor
+        self.actor_moe = MoE(
+            expert_num=expert_num,
+            input_dim=mlp_input_dim_a,
+            hidden_dims=actor_hidden_dims,
+            output_dim=num_actions,
+            activation=activation,
+        )
+
+        # Value function
+        self.critic_experts = Experts(
+            expert_num=expert_num,
+            input_dim=mlp_input_dim_c,
+            backbone_hidden_dims=critic_hidden_dims[:-1],
+            expert_hidden_dim=critic_hidden_dims[-1],
+            output_dim=1,
+            activation=activation,
+        )
+
+        print(f"Actor MoE: {self.actor_moe}")
+        print(f"Critic Experts: {self.critic_experts}")
+        print(f"Teacher Encoder: {self.teacher_encoder}")
+        print(f"Student MoE Encoder: {self.student_moe_encoder}")
+
+        self.distribution = None
+        self.std = nn.Parameter(init_noise_std * torch.ones(num_actions))
+        # disable args validation for speedup
+        Normal.set_default_validate_args = False
+
+    def reset(self, dones=None):
+        self.history[dones > 0] = 0.0
+
+    def forward(self):
+        raise NotImplementedError
+    
+    @property
+    def action_mean(self):
+        return self.distribution.mean
+
+    @property
+    def action_std(self):
+        return self.distribution.stddev
+    
+    @property
+    def entropy(self):
+        return self.distribution.entropy().sum(dim=-1)
+
+    def update_distribution(self, x):
+        mean, _ = self.actor_moe(x)
+        self.distribution = Normal(mean, mean*0. + self.std)
+
+    def act(self, obs, privileged_obs, history, is_teacher, **kwargs):
+        if is_teacher:
+            latent = self.teacher_encoder(privileged_obs)
+        else:
+            with torch.no_grad():
+                latent, _ = self.student_moe_encoder(history)
+        x = torch.cat([latent, obs], dim=1)
+        self.update_distribution(x)
+        return self.distribution.sample()
+    
+    def get_actions_log_prob(self, actions):
+        return self.distribution.log_prob(actions).sum(dim=-1)
+
+    def act_inference(self, obs):
+        self.history = torch.cat([self.history[:, 1:], obs.unsqueeze(1)], dim=1)
+        latent, _ = self.student_moe_encoder(self.history.flatten(1))
+        x = torch.cat([latent, obs], dim=1)
+        mean, _ = self.actor_moe(x)
+        return mean
+
+    def evaluate(self, obs, privileged_obs, history, is_teacher, **kwargs):
+        if is_teacher:
+            latent = self.teacher_encoder(privileged_obs)
+        else:
+            latent, _ = self.student_moe_encoder(history)
+        x_actor = torch.cat([latent, obs], dim=1)
+        weights = self.actor_moe.gating_network(x_actor)  # (B, expert_num)
+        x_critic = torch.cat([latent.detach(), privileged_obs], dim=1)
+        experts_value = self.critic_experts(x_critic)
+        value = torch.sum(weights.unsqueeze(-1) * experts_value, dim=1)
+        return value, weights

rsl_rl/rsl_rl/runners/on_policy_runner_cts.py

@@ -36,8 +36,8 @@ import statistics
 from torch.utils.tensorboard import SummaryWriter
 import torch
 
-from rsl_rl.algorithms import CTS, MoECTS, MCPCTS, ACMoECTS
-from rsl_rl.modules import ActorCriticCTS, ActorCriticMoECTS, ActorCriticMCPCTS, ActorCriticACMoECTS
+from rsl_rl.algorithms import CTS, MoECTS, MCPCTS, ACMoECTS, DualMoECTS
+from rsl_rl.modules import ActorCriticCTS, ActorCriticMoECTS, ActorCriticMCPCTS, ActorCriticACMoECTS, ActorCriticDualMoECTS
 from rsl_rl.env import VecEnv
 
 import yaml
@@ -79,7 +79,7 @@ class OnPolicyRunnerCTS:
             num_critic_obs = self.env.num_obs
         history_length = train_cfg["history_length"]
         actor_critic_class = eval(self.cfg["policy_class_name"])
-        model: Union[ActorCriticCTS, ActorCriticMoECTS, ActorCriticMCPCTS, ActorCriticACMoECTS] = actor_critic_class(
+        model: Union[ActorCriticCTS, ActorCriticMoECTS, ActorCriticMCPCTS, ActorCriticACMoECTS, ActorCriticDualMoECTS] = actor_critic_class(
             self.env.num_obs,
             num_critic_obs,
             self.env.num_actions,
@@ -87,7 +87,7 @@ class OnPolicyRunnerCTS:
             history_length,
             **self.policy_cfg).to(self.device)
         alg_class = eval(self.cfg["algorithm_class_name"])
-        self.alg: Union[CTS, MoECTS, MCPCTS, ACMoECTS] = alg_class(model, self.env.num_envs, history_length, device=self.device, **self.alg_cfg)
+        self.alg: Union[CTS, MoECTS, MCPCTS, ACMoECTS, DualMoECTS] = alg_class(model, self.env.num_envs, history_length, device=self.device, **self.alg_cfg)
         self.num_steps_per_env = self.cfg["num_steps_per_env"]
         self.save_interval = self.cfg["save_interval"]
 
@@ -113,7 +113,7 @@ class OnPolicyRunnerCTS:
         # robogauge client
         try:
             from robogauge.scripts.client import RoboGaugeClient
-            self.robogauge_client = RoboGaugeClient()
+            self.robogauge_client = RoboGaugeClient("http://127.0.0.1:9973")  # Change PORT to your server port if needed, default is 9973
         except:
             self.robogauge_client = None
     
@@ -176,7 +176,7 @@ class OnPolicyRunnerCTS:
 
                 # Learning step
                 start = stop
-                if self.cfg["algorithm_class_name"] == "ACMoECTS":
+                if self.cfg["algorithm_class_name"] in ["ACMoECTS", "DualMoECTS"]:
                     self.alg.compute_returns(obs, privileged_obs, self.history.flatten(1))
                 else:
                     self.alg.compute_returns(privileged_obs, self.history.flatten(1))
@@ -185,6 +185,8 @@ class OnPolicyRunnerCTS:
                 mean_value_loss, mean_surrogate_loss, mean_entropy_loss, mean_latent_loss = self.alg.update()
             elif self.cfg["algorithm_class_name"] in ["MoECTS", "ACMoECTS"]:
                 mean_value_loss, mean_surrogate_loss, mean_entropy_loss, mean_latent_loss, mean_load_balance_loss = self.alg.update()
+            elif self.cfg["algorithm_class_name"] == "DualMoECTS":
+                mean_value_loss, mean_surrogate_loss, mean_entropy_loss, mean_latent_loss, mean_load_balance_loss, mean_actor_load_balance_loss = self.alg.update()
             stop = time.time()
             learn_time = stop - start
             self.current_learning_iteration += 1
@@ -228,6 +230,8 @@ class OnPolicyRunnerCTS:
         self.writer.add_scalar('Loss/latent', locs['mean_latent_loss'], locs['it'])
         if 'mean_load_balance_loss' in locs:
             self.writer.add_scalar('Loss/load_balance', locs['mean_load_balance_loss'], locs['it'])
+        if 'mean_actor_load_balance_loss' in locs:
+            self.writer.add_scalar('Loss/actor_load_balance', locs['mean_actor_load_balance_loss'], locs['it'])
         self.writer.add_scalar('Loss/learning_rate', self.alg.learning_rate, locs['it'])
         if 'mcp' not in self.cfg["algorithm_class_name"].lower():
             self.writer.add_scalar('Policy/mean_noise_std', mean_std.item(), locs['it'])
@@ -257,6 +261,8 @@ class OnPolicyRunnerCTS:
                       f"""{'Latent loss:':>{pad}} {locs['mean_latent_loss']:.4f}\n""")
         if 'mean_load_balance_loss' in locs:
             log_string += f"""{'Load balance loss:':>{pad}} {locs['mean_load_balance_loss']:.4f}\n"""
+        if 'mean_actor_load_balance_loss' in locs:
+            log_string += f"""{'Actor load balance loss:':>{pad}} {locs['mean_actor_load_balance_loss']:.4f}\n"""
         if 'mcp' not in self.cfg["algorithm_class_name"].lower():
             log_string += f"""{'Mean action noise std:':>{pad}} {mean_std.item():.2f}\n"""
         if len(locs['teacher_rewbuffer']):