v0.1.6; add rem-cts

rsl_rl/rsl_rl/modules/__init__.py

@@ -34,4 +34,5 @@ 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
+from .actor_critic_dual_moe_cts import ActorCriticDualMoECTS
+from .actor_critic_rem_cts import ActorCriticREMCTS

rsl_rl/rsl_rl/modules/actor_critic_rem_cts.py

@@ -0,0 +1,141 @@
+# -*- 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    : Mixture of Experts Concurrent Teacher Student Network
+@Refer   : CTS https://arxiv.org/abs/2405.10830, Switch Transformers https://arxiv.org/abs/2101.03961
+'''
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributions import Normal
+
+from rsl_rl.modules.utils import L2Norm, SimNorm, StudentMoEEncoder, MLP
+
+class ActorCriticREMCTS(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],
+                        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_a = latent_dim + num_obs
+        mlp_input_dim_c = latent_dim + num_critic_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=activation),
+            L2Norm() if norm_type == 'l2norm' else SimNorm()
+        )
+
+        # Student MoE encoder
+        self.student_moe_encoder = StudentMoEEncoder(
+            expert_num=expert_num,
+            input_dim=mlp_input_dim_s,
+            hidden_dims=student_encoder_hidden_dims,
+            output_dim=latent_dim,
+            activation=activation,
+            norm_type=norm_type,
+        )
+
+        # Policy
+        self.actor = MLP([mlp_input_dim_a, *actor_hidden_dims, num_actions], activation=activation)
+
+        # Value function
+        self.critic = MLP([mlp_input_dim_c, *critic_hidden_dims, 1], activation=activation)
+
+        print(f"Actor MLP: {self.actor}")
+        print(f"Critic MLP: {self.critic}")
+        print(f"Teacher Encoder: {self.teacher_encoder}")
+        print(f"Student MoE Encoder: {self.student_moe_encoder}")
+
+        # Action noise
+        self.std = nn.Parameter(init_noise_std * torch.ones(num_actions))
+        self.distribution = None
+        # disable args validation for speedup
+        Normal.set_default_validate_args = False
+
+    @staticmethod
+    # not used at the moment
+    def init_weights(sequential, scales):
+        [torch.nn.init.orthogonal_(module.weight, gain=scales[idx]) for idx, module in
+         enumerate(mod for mod in sequential if isinstance(mod, nn.Linear))]
+
+
+    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, latent_and_obs):
+        mean = self.actor(latent_and_obs)
+        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)
+        actions_mean = self.actor(x)
+        return actions_mean
+
+    def evaluate(self, privileged_obs, history, is_teacher, **kwargs):
+        if is_teacher:
+            latent = self.teacher_encoder(privileged_obs)
+        else:
+            latent, _ = self.student_moe_encoder(history)
+        x = torch.cat([latent.detach(), privileged_obs], dim=1)
+        value = self.critic(x)
+        return value