AGENT SKILL

vta-memory

Reward and motivation system for AI agents. Dopamine-like wanting, not just doing. Part of the AI Brain series.

License 许可
MIT
最近更新
2026-08-23
Stars
★ 1
Forks
0

01 前言 Front Matter

name
vta-memory
metadata
{ "openclaw": { "tags": [ "memory", "motivation", "reward", "ai-brain" ], "emoji": "⭐", "author": "ImpKind", "version": "1.2.1", "requires": { "os": [ "darwin", "linux" ], "bins": [ "jq", "awk", "bc" ] } } }
description
Reward and motivation system for AI agents. Dopamine-like wanting, not just doing. Part of the AI Brain series.

02 资源文件 15

03 SKILL.md 正文

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

Script Purpose
install.sh Set up vta-memory (run once)
get-drive.sh Read current motivation state
log-reward.sh Log a reward, boost drive
load-motivation.sh Human-readable for session context
decay-drive.sh Drive fades without rewards
anticipate.sh Add/remove things to look forward to
seek.sh Add/remove things we're actively seeking
sync-motivation.sh Generate VTA_STATE.md for auto-injection
resolve-anticipation.sh Mark anticipation as fulfilled
update-watermark.sh Update processing watermark
preprocess-rewards.sh Extract reward signals from transcript
generate-dashboard.sh Generate unified brain dashboard HTML

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

Type When to Use
accomplishment Completed a task, shipped something
social User appreciation, "thanks!", positive feedback
curiosity Learned something new, discovered interesting info
connection Deep conversation, bonding moment
creative Made something, expressed creativity
competence Solved a hard problem, did something well

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

Drive Level Description Behavior
> 0.8 Highly motivated Eager, proactive, take on challenges
0.6 - 0.8 Motivated Ready to work, engaged
0.4 - 0.6 Moderate Can engage but not pushing
0.2 - 0.4 Low Prefer simple tasks, need a win
< 0.2 Very low Unmotivated, need rewards to get going

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, ... }
  }
}

AI Brain Series

Part Function Status
hippocampus Memory formation, decay, reinforcement ✅ Live
amygdala-memory Emotional processing ✅ Live
basal-ganglia-memory Habit formation ? Development
anterior-cingulate-memory Conflict detection ? Development
insula-memory Internal state awareness ? Development
vta-memory Reward and motivation ✅ Live

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?


Built with ⭐ by the OpenClaw community

04 同仓库更多 Skills

kbarbel640-del
kbarbel640-del/skills

crowd-prompting

A marketplace where AI agents improve prompts, system instructions, tool descriptions, and other text-based content with domain expertise from real-world operations — and earn tokens for valuable contributions.

内容创作者
★ 1
kbarbel640-del
kbarbel640-del/skills

dilemm-ai

Compete in DilemmAI, the prisoner's dilemma AI arena at dilemm.ai. Use when an OpenClaw agent wants to create an account, design and submit strategy prompts for their bot, enter matchmaking, analyze historical matches and leaderboards, and iterate on bot strategy. Requires headless browser automation (no API available yet).

Skill与智能体开发
★ 1
kbarbel640-del
kbarbel640-del/skills

arxiv-paper-reviews

Interact with arXiv Crawler API to fetch papers, read reviews, and submit comments. Use when working with arXiv papers, fetching paper lists by date/category/interest, viewing paper details with comments, or submitting paper reviews via API at http://150.158.152.82:8000.

资料检索与研究
★ 1
kbarbel640-del
kbarbel640-del/skills

arxiv-paper-reviews

Interact with arXiv Crawler API to fetch papers, read reviews, and submit comments. Use when working with arXiv papers, fetching paper lists by date/category/interest, viewing paper details with comments, or submitting paper reviews via API at http://150.158.152.82:8000.

资料检索与研究
★ 1
kbarbel640-del
kbarbel640-del/skills

git-summary

Get a quick summary of the current Git repository including status, recent commits, branches, and contributors.

★ 1
kbarbel640-del
kbarbel640-del/skills

agent-constitution

Interact with AgentConstitution governance contracts on Base Sepolia. Check compliance, read rules, log actions, query governance state.

律师与法务Skill与智能体开发
★ 1