Vta Memory

# 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

```bash 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

```bash ./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

```bash ./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

```bash ./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:

```bash ./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

```json { "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:

```bash # 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`: ```json {"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?

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*Built with ⭐ by the OpenClaw community*