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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

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, ... }
  }
}

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

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?

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Built with ⭐ by the OpenClaw community