---
title: World Model Demo
emoji: 🧠
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
tags:
  - world-model
  - reinforcement-learning
  - planning
  - ai-education
  - model-based-rl
  - muzero
  - dreamer
---

# 🧠 World Model Demo

**An interactive visualization of model-based reinforcement learning concepts**

## What is a World Model?

A **world model** is an internal representation that an AI agent uses to *simulate* the environment without actually interacting with it. Think of it as the agent's "imagination" - it can mentally rehearse actions and predict their outcomes before committing to them in the real world.

### The Key Insight

Instead of learning through pure trial-and-error (which is slow and potentially dangerous), an agent with a world model can:

1. **Imagine** possible futures by simulating "what if I do X?"
2. **Evaluate** which imagined future looks best
3. **Plan** a sequence of actions to reach that future
4. **Act** with confidence, having already "seen" the outcome

## How This Differs from Language Models

| Aspect | Language Model (GPT, Claude) | World Model (MuZero, Dreamer) |
|--------|------------------------------|-------------------------------|
| **Primary function** | Predict next token in a sequence | Predict next *state* given an action |
| **Training signal** | Text prediction loss | Reward from environment |
| **"Imagination"** | Generates plausible text continuations | Simulates future environment states |
| **Planning** | Implicit (via chain-of-thought) | Explicit (via tree search or rollouts) |
| **Grounding** | Statistical patterns in text | Causal dynamics of an environment |

### A Concrete Example

**Language Model**: "If I push a ball off a table, it will..." → generates plausible text based on patterns

**World Model**: Given state (ball on table) + action (push) → predicts new state (ball falling, trajectory, landing position) with enough fidelity to *plan* around it

## What You're Seeing in This Demo

This visualization shows a simplified world model operating on a grid navigation task:

### The Four Phases

1. **🔍 Observe**: The agent perceives the current grid state (its position, goal location, obstacles)

2. **💭 Imagine**: The world model predicts what would happen for each possible action (up/down/left/right). You see this as the "mental simulation" exploring future states.

3. **🌳 Plan**: Using tree search (similar to how chess engines work), the agent evaluates sequences of actions by imagining multiple steps ahead. Better paths to the goal get higher scores.

4. **⚡ Act**: The agent executes the best action found during planning, then the cycle repeats.

### Why This Matters for AI Safety

World models are crucial for AI safety research because:

- **Predictability**: Agents that plan can be analyzed - we can inspect what futures they're considering
- **Corrigibility**: Planning agents can incorporate "don't do irreversible things" into their search
- **Interpretability**: The world model's predictions can be examined for accuracy and bias
- **Scalable oversight**: Humans can audit the agent's "reasoning" by inspecting its simulated futures

## Real-World Architectures

This demo is inspired by:

- **MuZero** (DeepMind): Learned world models that mastered Go, chess, and Atari without knowing the rules
- **Dreamer** (Hafner et al.): World models for continuous control from pixels
- **IRIS** (Micheli et al.): Transformer-based world models for Atari
- **Genie** (DeepMind): Generative world models from video

## Try It Yourself

1. Click **"Run World Model"** to watch the full planning cycle
2. Use **Step Mode** to see each phase individually  
3. Adjust grid size and obstacles to see how planning adapts
4. Watch the **Imagined Futures** panel to see the agent's "thoughts"

---

*Created by [Anthony Maio](https://huggingface.co/anthonym21) as an educational resource for AI safety research*