Initial robot vacuum code

agent_diy/workflow/train_workflow.py

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+#!/usr/bin/env python3
+# -*- coding: UTF-8 -*-
+###########################################################################
+# Copyright © 1998 - 2026 Tencent. All Rights Reserved.
+###########################################################################
+"""
+Author: Tencent AI Arena Authors
+"""
+
+
+import time
+from common_python.utils.common_func import Frame
+from agent_diy.feature.definition import (
+    sample_process,
+    reward_shaping,
+)
+from tools.train_env_conf_validate import read_usr_conf
+from tools.metrics_utils import get_training_metrics
+from common_python.utils.workflow_disaster_recovery import handle_disaster_recovery
+
+
+def workflow(envs, agents, logger=None, monitor=None, *args, **kwargs):
+    env, agent = envs[0], agents[0]
+
+    # Read and validate configuration file
+    # 配置文件读取和校验
+    usr_conf = read_usr_conf("agent_diy/conf/train_env_conf.toml", logger)
+    if usr_conf is None:
+        logger.error(f"usr_conf is None, please check agent_diy/conf/train_env_conf.toml")
+        return
+
+    # Please write your DIY training process below.
+    # 请在下方写你DIY的训练流程
+
+    # At the start of each game, support loading the latest model file
+    # 每次对局开始时, 支持加载最新model文件, 该调用会从远程的训练节点加载最新模型
+    agent.load_model(id="latest")
+
+    # Model saving
+    # 保存模型
+    agent.save_model()
+
+    return