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library_name: hd-ppo
tags:
- Acrobot-v1
- deep-reinforcement-learning
- reinforcement-learning
- hyperdimensional-computing
- fractional-power-encoding
- LTU-AI
model-index:
- name: HD-PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Acrobot-v1
type: Acrobot-v1
metrics:
- type: mean_reward
value: -84.60 +/- 11.41
name: mean_reward
verified: false
---
# **HD-PPO** Agent playing **Acrobot-v1**
This is a trained **HD-PPO** (Hyperdimensional Proximal Policy Optimization) agent
playing **Acrobot-v1** using **gradient-adaptive Fractional Power Encoding (FPE)**
with a prune-and-fine-tune pipeline.
Published by [LTU-AI](https://huggingface.co/LTU-AI).
## Pipeline
1. Train a teacher at **D=256** with gradient-adaptive single-beta FPE.
2. Prune by actor-weight importance through **D=256 → 64 → 16**.
3. Fine-tune each pruned checkpoint with PPO.
Published checkpoint: seed **123**, compact **D=16** model
(held-out eval mean reward **-84.60 ± 11.41**).
## Usage
Install dependencies:
```bash
pip install -r requirements.txt
```
Evaluate the local checkpoint:
```bash
python enjoy.py --weights hdppo-Acrobot-v1/weights.npz --episodes 10
```
Render episodes:
```bash
python enjoy.py --weights hdppo-Acrobot-v1/weights.npz --render --episodes 3
```
Record a replay video:
```bash
python record_video.py --weights hdppo-Acrobot-v1/weights.npz --output replay.mp4
```
Load from Hugging Face Hub:
```bash
python enjoy.py --weights LTU-AI/hdppo-Acrobot-v1 --episodes 10
```
## Training pipeline
Reproduce the teacher → prune → fine-tune workflow:
```bash
python run_prune_finetune_5seed.py
```
## Hyperparameters
```python
{
"env": "Acrobot-v1",
"algo": "HD-PPO (gradient-adaptive FPE, discrete)",
"teacher_D": 256,
"pruned_D": 16,
"timesteps_per_stage": 1000000,
"seed": 123
}
```
## Environment Arguments
```python
{
"render_mode": "rgb_array"
}
```
## Model files
| File | Description |
|------|-------------|
| `hdppo-Acrobot-v1/weights.npz` | Published actor (+ critic if HD) and FPE encoder (D=16) |
| `hdppo-Acrobot-v1/weights_D256_teacher.npz` | Teacher checkpoint (D=256) |
| `replay.mp4` | Sample rollout video from the published min-D checkpoint |
| `results.json` | Evaluation summary for the published checkpoint |
| `results_D256_teacher.json` | Evaluation summary for the teacher |
| `config.yml` | Training hyperparameters |
| `train_hdppo.py` / training modules | Self-contained training code |
## Citation
If you use this model, please cite the HD-PPO / Hybrid-HD-PPO work.
|