Initial robot vacuum code

agent_ppo/model/model.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+# -*- coding: UTF-8 -*-
+###########################################################################
+# Copyright © 1998 - 2026 Tencent. All Rights Reserved.
+###########################################################################
+"""
+Author: Tencent AI Arena Authors
+
+Simple MLP policy network for Robot Vacuum.
+清扫大作战策略网络。
+"""
+
+import torch
+import torch.nn as nn
+
+from agent_ppo.conf.conf import Config
+
+
+def _make_fc(in_dim, out_dim, gain=1.41421):
+    """Create a linear layer with orthogonal initialization.
+
+    创建正交初始化的线性层。
+    """
+    layer = nn.Linear(in_dim, out_dim)
+    nn.init.orthogonal_(layer.weight, gain=gain)
+    nn.init.zeros_(layer.bias)
+    return layer
+
+
+class Model(nn.Module):
+    """Dual-head MLP for Robot Vacuum.
+
+    清扫大作战双头 MLP 策略网络。
+    """
+
+    def __init__(self, device=None):
+        super().__init__()
+        self.model_name = "robot_vacuum"
+        self.device = device
+
+        obs_dim = Config.DIM_OF_OBSERVATION  # 69
+        act_num = Config.ACTION_NUM  # 8
+
+        # Shared backbone / 共享骨干网络
+        self.backbone = nn.Sequential(
+            _make_fc(obs_dim, 128),
+            nn.ReLU(),
+            _make_fc(128, 64),
+            nn.ReLU(),
+        )
+
+        # Actor head: outputs action logits / 策略头：输出动作 logits
+        self.actor_head = _make_fc(64, act_num, gain=0.01)
+
+        # Critic head: outputs single state value / 价值头：输出单个状态价值
+        self.critic_head = _make_fc(64, 1, gain=0.01)
+
+    def forward(self, s, inference=False):
+        """Forward pass.
+
+        前向传播。
+        """
+        x = s.to(torch.float32)
+        h = self.backbone(x)
+        logits = self.actor_head(h)
+        value = self.critic_head(h)
+        return [logits, value]
+
+    def set_train_mode(self):
+        self.train()
+
+    def set_eval_mode(self):
+        self.eval()