Initial robot vacuum code

agent_diy/agent.py

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+#!/usr/bin/env python3
+# -*- coding: UTF-8 -*-
+###########################################################################
+# Copyright © 1998 - 2026 Tencent. All Rights Reserved.
+###########################################################################
+"""
+Author: Tencent AI Arena Authors
+
+Robot Vacuum DIY Agent class based on kaiwudrl BaseAgent interface.
+清扫大作战 DIY Agent 主类，基于 kaiwudrl BaseAgent 接口。
+"""
+
+
+import torch
+from kaiwudrl.interface.agent import BaseAgent
+from agent_diy.model.model import Model
+from agent_diy.conf.conf import Config
+
+
+class Agent(BaseAgent):
+    def __init__(self, agent_type="player", device=None, logger=None, monitor=None):
+        """Initialize the agent.
+
+        初始化 Agent。
+        """
+        super().__init__(agent_type, device, logger, monitor)
+
+    def predict(self, list_obs_data):
+        """Predict action from observation data.
+
+        根据观测数据推理动作。
+        """
+        pass
+
+    def exploit(self, list_obs_data):
+        """Evaluation mode inference (greedy).
+
+        评估模式推理（贪心）。
+        """
+        pass
+
+    def learn(self, list_sample_data):
+        """Train the model.
+
+        训练模型。
+        """
+        pass
+
+    def save_model(self, path=None, id="1"):
+        """Save model checkpoint.
+
+        保存模型检查点。
+        """
+        pass
+
+    def load_model(self, path=None, id="1"):
+        """Load model checkpoint.
+
+        加载模型检查点。
+        """
+        pass
+
+    def observation_process(self, obs, preprocessor, extra_info=None):
+        """
+        This function is an important feature processing function, mainly responsible for:
+            - Parsing information in the raw data
+            - Parsing preprocessed feature data
+            - Processing the features and returning the processed feature vector
+            - Concatenation of features
+            - Annotation of legal actions
+        Function inputs:
+            - obs: Local observation information returned by the environment
+            - preprocessor: Preprocessor
+            - extra_info: Global information returned by the environment
+        Function outputs:
+            - ObsData: Observation data for model inference
+            - remain_info: Other data for reward calculation
+
+        该函数是特征处理的重要函数, 主要负责：
+            - 解析原始数据里的信息
+            - 解析预处理后的特征数据
+            - 对特征进行处理, 并返回处理后的特征向量
+            - 特征的拼接
+            - 合法动作的标注
+        函数的输入：
+            - obs: 环境返回的局部观测信息
+            - preprocessor: 预处理器
+            - extra_info: 环境返回的全局状态信息
+        函数的输出：
+            - ObsData: 用于模型推理的观测数据
+            - remain_info: 用于奖励计算的其他数据
+        """
+        pass
+
+    def action_process(self, act_data):
+        pass

agent_diy/algorithm/algorithm.py

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+#!/usr/bin/env python3
+# -*- coding: UTF-8 -*-
+###########################################################################
+# Copyright © 1998 - 2026 Tencent. All Rights Reserved.
+###########################################################################
+"""
+Author: Tencent AI Arena Authors
+
+Robot Vacuum DIY algorithm implementation.
+清扫大作战 DIY 算法实现。
+"""
+
+
+class Algorithm:
+    """DIY algorithm class.
+
+    DIY 算法类。
+    """
+
+    def __init__(self, model, optimizer, scheduler, device=None, logger=None, monitor=None):
+        """Initialize the algorithm.
+
+        初始化算法。
+        """
+        pass
+
+    def learn(self, list_sample_data):
+        """Training entry.
+
+        训练入口。
+        """
+        pass

agent_diy/conf/conf.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 numpy as np
+
+
+# Configuration, including dimension settings and algorithm parameter settings.
+# 配置，包含维度设置，算法参数设置
+class Config:
+
+    # Whether to use CNN networks
+    # 是否使用CNN网络
+    USE_CNN = False
+    VIEW_SIZE = 50 if USE_CNN else 0
+
+    FEATURE_VECTOR_SHAPE = (153,)
+    FEATURE_IMAGE_SHAPE = (4, VIEW_SIZE + 1, VIEW_SIZE + 1)
+
+    ACTION_SHAPE = (8,)
+    VALUE_SHAPE = (1,)
+
+    # Discount factor GAMMA in RL
+    # RL中的回报折扣GAMMA
+    GAMMA = 0.95
+
+    # Initial learning rate
+    # 初始的学习率
+    START_LR = 5e-4
+
+    # Value function loss coefficient
+    # 价值函数损失系数
+    VALUE_LOSS_COEFF = 0.5
+
+    # Entropy regularization coefficient
+    # 熵正则化系数
+    ENTROPY_LOSS_COEFF = 0.025

agent_diy/conf/monitor_builder.py

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+#!/usr/bin/env python3
+# -*- coding: UTF-8 -*-
+###########################################################################
+# Copyright © 1998 - 2026 Tencent. All Rights Reserved.
+###########################################################################
+"""
+Author: Tencent AI Arena Authors
+
+Monitor panel configuration builder for Robot Vacuum.
+清扫大作战监控面板配置构建器。
+"""
+
+
+from kaiwudrl.common.monitor.monitor_config_builder import MonitorConfigBuilder
+
+
+def build_monitor():
+    """
+    This function is used to create monitoring panel configurations for custom indicators.
+    该函数用于创建自定义指标的监控面板配置。
+    """
+    monitor = MonitorConfigBuilder()
+
+    config_dict = (
+        monitor.title("扫地机器人")
+        .add_group(
+            group_name="算法指标",
+            group_name_en="algorithm",
+        )
+        .add_panel(
+            name="累积回报",
+            name_en="reward",
+            type="line",
+        )
+        .add_metric(
+            metrics_name="reward",
+            expr="avg(reward{})",
+        )
+        .end_panel()
+        .add_panel(
+            name="总损失",
+            name_en="total_loss",
+            type="line",
+        )
+        .add_metric(
+            metrics_name="total_loss",
+            expr="avg(total_loss{})",
+        )
+        .end_panel()
+        .add_panel(
+            name="价值损失",
+            name_en="value_loss",
+            type="line",
+        )
+        .add_metric(
+            metrics_name="value_loss",
+            expr="avg(value_loss{})",
+        )
+        .end_panel()
+        .add_panel(
+            name="策略损失",
+            name_en="policy_loss",
+            type="line",
+        )
+        .add_metric(
+            metrics_name="policy_loss",
+            expr="avg(policy_loss{})",
+        )
+        .end_panel()
+        .add_panel(
+            name="熵损失",
+            name_en="entropy_loss",
+            type="line",
+        )
+        .add_metric(
+            metrics_name="entropy_loss",
+            expr="avg(entropy_loss{})",
+        )
+        .end_panel()
+        .end_group()
+        .build()
+    )
+    return config_dict

agent_diy/conf/train_env_conf.toml

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+[env_conf]
+# Maps used for training. Customize by keeping only desired map IDs, e.g. [1, 2] for maps 1 and 2.
+# 训练使用的地图。可自定义选择期望用来训练的地图，如只期望使用1、2号地图训练数组内仅保留[1,2]即可。
+map = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+
+# Whether to randomly select maps. Boolean. 
+# true = randomly pick one from configured maps per episode, false = used sequentially.
+# 是否随机抽取地图。布尔值。true表示每局从配置的地图中随机抽取一张，false表示按顺序抽取地图训练。
+map_random = false
+
+# Number of official robots. Range: 1~4 (integer). 
+# In each round, official robots will be randomly generated on the road according to the configured.
+# 官方机器人数量。可配置范围为1～4（整数）。每局将按照配置数量在道路上随机生成官方机器人。
+robot_count = 4
+
+# Number of chargers. Range: 1~4 (integer). When less than 4, spawn points are randomly chosen.
+# 充电桩数量。可配置范围为1～4（整数）。当配置小于4时，将从每张地图可生成充电桩的点位随机选择对应数量的点位生成。
+charger_count = 4
+
+# Maximum steps. The task ends when the predicted steps in a single round reach the maximum. Range: 1~2000.
+# 最大步数。单局任务预测步数达到最大步数时，任务结束。可配置范围为1～2000。
+max_step = 1000
+
+# Maximum battery. The battery level when fully charged. Range: 100~999.
+# 最大电量。满电状态下的电量。可配置范围100～999。
+battery_max = 200

agent_diy/feature/definition.py

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+#!/usr/bin/env python3
+# -*- coding: UTF-8 -*-
+###########################################################################
+# Copyright © 1998 - 2026 Tencent. All Rights Reserved.
+###########################################################################
+"""
+Author: Tencent AI Arena Authors
+"""
+
+
+from common_python.utils.common_func import create_cls
+import numpy as np
+from agent_diy.conf.conf import Config
+
+# The create_cls function is used to dynamically create a class. The first parameter of the function is the type name,
+# and the remaining parameters are the attributes of the class, which should have a default value of None.
+# create_cls函数用于动态创建一个类，函数第一个参数为类型名称，剩余参数为类的属性，属性默认值应设为None
+ObsData = create_cls(
+    "ObsData",
+    feature=None,
+    legal_act=None,
+)
+
+
+ActData = create_cls(
+    "ActData",
+    act=None,
+)
+
+
+# SampleData is used to transfer training samples between aisrv and learner.
+# SampleData用于在aisrv和learner之间传递训练样本
+SampleData = create_cls(
+    "SampleData",
+    obs=153,  # Observation dimension / 观测维度
+    legal_actions=8,  # Legal action dimension / 合法动作维度
+    actions=1,  # Action dimension / 动作维度
+    probs=8,  # Action probability distribution dimension / 动作概率分布维度
+    rewards=1,  # Reward / 奖励
+    advantages=1,  # Advantage function / 优势函数
+    values=1,  # Value function / 价值函数
+    dones=1,  # Whether terminated / 是否结束
+)
+
+
+def reward_shaping(frame_no, score, terminated, truncated, remain_info, _remain_info, obs, _obs):
+    """Reward shaping function.
+
+    奖励塑形函数。
+    """
+    pass
+
+
+def sample_process(list_game_data):
+    """Sample processing function.
+
+    样本处理函数。
+    """
+    pass

agent_diy/model/model.py

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+#!/usr/bin/env python3
+# -*- coding: UTF-8 -*-
+###########################################################################
+# Copyright © 1998 - 2026 Tencent. All Rights Reserved.
+###########################################################################
+"""
+Author: Tencent AI Arena Authors
+
+Robot Vacuum DIY model implementation.
+清扫大作战 DIY 模型实现。
+"""
+
+
+import torch
+import numpy as np
+from torch import nn
+import torch.nn.functional as F
+
+
+class Model(nn.Module):
+    """DIY model class.
+
+    DIY 模型类。
+    """
+
+    def __init__(self, state_shape, action_shape=0, softmax=False):
+        """Initialize the model.
+
+        初始化模型。
+        """
+        super().__init__()
+
+        # User-defined network
+        # 用户自定义网络

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