v1.2.0

Vta Memory

ImpKind ImpKind ← All skills

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

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Updated
2026-02-24

Install

npx clawhub@latest install vta-memory

Documentation

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](https://www.clawhub.ai/skills/hippocampus) | Memory formation, decay, reinforcement | ✅ Live |

| [amygdala-memory](https://www.clawhub.ai/skills/amygdala-memory) | Emotional processing | ✅ Live |

| [basal-ganglia-memory](https://www.clawhub.ai/skills/basal-ganglia-memory) | Habit formation | 🚧 Development |

| [anterior-cingulate-memory](https://www.clawhub.ai/skills/anterior-cingulate-memory) | Conflict detection | 🚧 Development |

| [insula-memory](https://www.clawhub.ai/skills/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*

Launch an agent with Vta Memory on Termo.