# Robo Dopamine Rl

> Overcome reward function design challenges via General Reward Model (GRM) for step-wise progress assessment. Uses multi-view observations for occlusion robustness, hop-based progress normalization, Policy-Invariant Reward Shaping—enabling 95% robot task success within 150 interactions with theoretically-grounded dense rewards.

- Skill: `adu2021/robo-dopamine-rl` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/robo-dopamine-rl`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/robo-dopamine-rl/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/robo-dopamine-rl

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## Overview

Addresses reward design challenges in robotic manipulation via principled progress modeling.

## Core Technique

**General Reward Model:**

```python
# Hop-based progress: normalized by task span
progress = model.predict_progress(observation, task_span)

# Multi-view fusion
fused_progress = aggregate_views([view1, view2, view3])

# Policy-Invariant Reward Shaping
reward = outcome_reward + policy_invariant_shaping(fused_progress)
```

## Performance

- 92.8% task completion accuracy
- 95% success within 150 interactions
- Theoretically grounded reward shaping

## References

- Step-wise progress discretization
- Multi-view observation fusion
- Policy-Invariant Reward Shaping