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WorldModels-A3C

★ 33 Open GitHub ↗

World Models with A3C on Carracing-v0 in gym

// repository documentation

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# World Models A3C

## Implementation of a variant of World Models

![](https://raw.githubusercontent.com/Deepest-Project/WorldModels-A3C/master/assets/world-models.png)

## Note
- Replaced MDN-RNN to LSTM for Memory
- Replaced CMA-ES to A3C for Controller
- Trained over two stages
    - Stage 1: V and M were trained on dataset with random rollout
    - Stage 2: V and M were trained on dataset with a3c rollout

## Training Result

Result with dataset using random rollout

<img src="/assets/scores.png" width="400">

Result with dataset using the pretrained model rollout

Play Demo

---

## Environment Setting
    apt-get update
    apt-get install swig
    pip install gym[box2d]

## Training Stage I
### Dataset Generation using Rollout with random policy
    python rollout.py

### Vision model with VAE
    python train-vae.py

### Memory model with LSTM-RNN
    python train-rnn.py

### Controller with A3C
    python train-a3c.py

## Training Stage II
### Rollout with the pretrained model
    python rollout-a3c.py

### Fine-tuning V and M with new dataset
    vi hparams.py
        extra = True

python train-vae.py
    python train-rnn.py

### Train new C with the improved V and M
    python train-a3c.py

## Test
 # <# of plays> <seed> <is_record>
 python test.py 2 999 False

## Reference
- https://arxiv.org/abs/1803.10122
- https://github.com/ctallec/world-models

WorldModels-A3C

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