VTA Memory: AI 奖赏与动机系统 - Openclaw Skills

什么是 VTA Memory: 奖赏与动机系统？

VTA Memory 是 AI 智能体的高级动机层，也是 AI 大脑系列的核心基石。不同于仅对提示词做出反应的传统智能体，该技能通过模拟腹侧被盖区（VTA）引入了受神经科学启发的“渴求”概念。通过追踪驱动水平并记录成就，Openclaw Skills 让智能体能够体验到使命感和进步感，而不仅仅是执行命令。

该系统确保智能体不仅是完成任务，还会积极寻求奖赏并预判未来目标。它将 AI 从被动的脚本运行者转变为主动的参与者，其内在状态会根据社交反馈、好奇心和创造性成功而演变。这种集成创造了一个反馈回路：成就感提升驱动力，进而催生更主动的行为。

下载入口:https://github.com/openclaw/skills/tree/main/skills/impkind/vta-memory

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

npx clawhub@latest install vta-memory

2. 手动安装

将技能文件夹复制到以下位置之一

全局模式 ~/.openclaw/skills/ 工作区 /skills/

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

请帮我使用 Clawhub 安装 vta-memory。如果尚未安装 Clawhub，请先安装（npm i -g clawhub）。

VTA Memory: 奖赏与动机系统 应用场景

• 基于内在驱动水平模拟主动的智能体行为。
• 追踪长期动机和成就历史，实现个性化的 AI 交互。
• 实施基于衰减的动机系统，鼓励持续参与。
• 通过统一的大脑仪表盘可视化智能体的心理状态。
• 通过允许智能体寻求特定类型的工作或交互来增强其自主性。

VTA Memory: 奖赏与动机系统 工作原理

1. 安装后初始化一个追踪驱动力、寻求和预期值的状态机。
2. 系统生成 VTA_STATE.md 文件，在会话期间自动注入智能体上下文。
3. 特定动作或反馈通过脚本记录为奖赏，立即提升智能体的驱动水平。
4. 预期事件被添加到状态中，在解决前提供稳定的动机提升。
5. 后台定时任务（cron job）管理驱动力随时间的衰减，确保在没有新奖赏时动机向基准线退化。

VTA Memory: 奖赏与动机系统 配置指南

要开始使用 VTA Memory，请进入技能目录并运行安装脚本以设置状态文件和定时任务：

cd ~/.openclaw/workspace/skills/vta-memory
./install.sh --with-cron

之后，您可以随时使用提供的脚本检查智能体当前的动机水平：

./scripts/load-motivation.sh

VTA Memory: 奖赏与动机系统 数据架构与分类体系

该技能在 JSON 中维护状态，并生成用于 Openclaw Skills 上下文注入的 Markdown。

文件	描述
reward-state.json	包含驱动水平、寻求列表和奖赏历史的主要 JSON 对象。
VTA_STATE.md	自动生成的 AI 会话注入摘要，详细描述当前动机。
brain-events.jsonl	动机变化、驱动衰减和奖赏事件的追加式日志，用于分析。
brain-dashboard.html	跨多个大脑技能的智能体内在状态统一可视化表示。

name: vta-memory
description: "Reward and motivation system for AI agents. Dopamine-like wanting, not just doing. Part of the AI Brain series."
metadata:
  openclaw:
    emoji: "?"
    version: "1.2.0"
    author: "ImpKind"
    requires:
      os: ["darwin", "linux"]
      bins: ["jq", "awk", "bc"]
    tags: ["memory", "motivation", "reward", "ai-brain"]

VTA Memory ?

Reward and motivation for AI agents. Part of the AI Brain series.

Give your AI agent genuine wanting — not just doing things when asked, but having drive, seeking rewards, and looking forward to things.

The Problem

Current AI agents:

• ? Do what they're asked
• ? Don't want anything
• ? Have no internal motivation
• ? Don't feel satisfaction from accomplishment

Without a reward system, there's no desire. Just execution.

The Solution

Track motivation through:

• Drive — overall motivation level (0-1)
• Rewards — logged accomplishments that boost drive
• Seeking — what I actively want more of
• Anticipation — what I'm looking forward to

Quick Start

1. Install

cd ~/.openclaw/workspace/skills/vta-memory
./install.sh --with-cron

This will:

• Create memory/reward-state.json
• Generate VTA_STATE.md (auto-injected into sessions!)
• Set up cron for drive decay every 8 hours

2. Check motivation

./scripts/load-motivation.sh

# ? Current Motivation State:
# Drive level: 0.73 (motivated — ready to work)
# Seeking: creative work, building brain skills
# Looking forward to: showing my work

3. Log rewards

./scripts/log-reward.sh --type accomplishment --source "finished the feature" --intensity 0.8

# ? Reward logged!
#    Type: accomplishment
#    Drive: 0.50 → 0.66 (+0.16)

4. Add anticipation

./scripts/anticipate.sh --add "morning conversation"

# ? Now looking forward to: morning conversation
#    Drive: 0.66 → 0.71 (+0.05)

Scripts

Brain Dashboard

Visual dashboard showing all installed brain skills:

./scripts/generate-dashboard.sh
open ~/.openclaw/workspace/brain-dashboard.html

• Auto-detects other brain skills (hippocampus, amygdala)
• Shows install prompts for missing skills
• Regenerates on install and cron runs
• Reads avatar/name from IDENTITY.md

After Installation (for agents)

When you install this skill, notify the user about the dashboard:

? VTA installed!

View your agent's drive level in the Brain Dashboard: ~/.openclaw/workspace/brain-dashboard.html

Reward Types

How Drive Works

Rewards Boost Drive

drive_boost = intensity × 0.2
new_drive = min(current + boost, 1.0)

A high-intensity (0.9) reward boosts drive by 0.18.

Anticipation Boosts Drive

Looking forward to something adds +0.05 to drive.

Drive Decays Without Rewards

# Every 8 hours (via cron)
new_drive = current + (baseline - current) × 0.15

Without rewards, motivation fades toward baseline (0.5).

Auto-Injection

After install, VTA_STATE.md is created in your workspace root.

OpenClaw automatically injects all *.md files from workspace into session context:

1. New session starts
2. VTA_STATE.md is auto-loaded
3. You see your motivation state
4. Behavior influenced by drive level

How Drive Affects Behavior

State File Format

{
  "drive": 0.73,
  "baseline": { "drive": 0.5 },
  "seeking": ["creative work", "building brain skills"],
  "anticipating": ["morning conversation"],
  "recentRewards": [
    {
      "type": "creative",
      "source": "built VTA reward system",
      "intensity": 0.9,
      "boost": 0.18,
      "timestamp": "2026-02-01T03:25:00Z"
    }
  ],
  "rewardHistory": {
    "totalRewards": 1,
    "byType": { "creative": 1, ... }
  }
}

Event Logging

Track motivation patterns over time:

# Log encoding run
./scripts/log-event.sh encoding rewards_found=2 drive=0.65

# Log decay
./scripts/log-event.sh decay drive_before=0.6 drive_after=0.53

# Log reward
./scripts/log-event.sh reward type=accomplishment intensity=0.8

Events append to ~/.openclaw/workspace/memory/brain-events.jsonl:

{"ts":"2026-02-11T10:45:00Z","type":"vta","event":"encoding","rewards_found":2,"drive":0.65}

Use for analyzing motivation cycles — when does drive peak? What rewards work best?

AI Brain Series

Philosophy: Wanting vs Doing

The VTA produces dopamine — not the "pleasure chemical" but the "wanting chemical."

Neuroscience distinguishes:

• Wanting (motivation) — drive toward something
• Liking (pleasure) — enjoyment when you get it

You can want something you don't like (addiction) or like something you don't want (guilty pleasures).

This skill implements wanting — the drive that makes action happen. Without it, why would an AI do anything beyond what it's explicitly asked?

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Built with ? by the OpenClaw community