PyTorch implementation for paper StARformer: Transformer with State-Action-Reward Representations.

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Deep Learning StARformer
Overview

StARformer

This repository contains the PyTorch implementation for our paper titled StARformer: Transformer with State-Action-Reward Representations. We learn local State-Action-Reward representations (StAR-representations) to improve (long) sequence modeling for reinforcement learning (and imitation learning).

Results

Installation

Dependencies can be installed by Conda:

conda env create -f my_env.yml

And install Atari ROMs.

Datasets

Please follow this instruction for datasets.

Example usage

See run.sh or below:

python run_star_atari.py --seed 123 --data_dir_prefix [data_directory] --epochs 10 --num_steps 500000 --num_buffers 50 --batch_size 64 --seq_len 30 --model_type 'star' --game 'Breakout'

[data_directory] is where you place the Atari dataset.

Variants (model_type):

  • 'star' (imitation)
  • 'star_rwd' (offline RL)
  • 'star_fusion' (see Figure 4a in our paper)
  • 'star_stack' (see Figure 4b in our paper)

Acknowledgement

This code is based on Decision-Transformer.

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Comments
  • Would the image based gym code also be released?

    Would the image based gym code also be released?

    Hi, thanks for open source the codebase. It looks to me that the gym code is using the state directly (not from image). https://github.com/elicassion/StARformer/blob/main/gym/models/starformer.py#L266 Would the image based gym code also be released? Thanks

    opened by Wangweiyao 1
Owner
Jinghuan Shang
CS Ph.D. student at Stony Brook; Full-stack;
Jinghuan Shang
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