Public implementation of the Contrastive Decision Transformers (ConDT), CoRL 2022
We build our Atari implementation on top of minGPT and the original decision transformer and benchmark our results on the DQN-replay dataset.
Dependencies can be installed with the following command:
conda env create -f conda_env_gpu.yml
Create a directory for the dataset and load the dataset using gsutil. Replace [DIRECTORY_NAME] and [GAME_NAME] accordingly (e.g., ./dqn_replay for [DIRECTORY_NAME] and Breakout for [GAME_NAME])
mkdir [DIRECTORY_NAME]
gsutil -m cp -R gs://atari-replay-datasets/dqn/[GAME_NAME] [DIRECTORY_NAME]
Script to reproduce ConDT results can be run as (the seed argument defines the seed used for the environment during evaluation):
python run_dt_atari.py --seed 123 --block_size 90 --epochs 5 --model_type 'reward_conditioned' --num_steps 500000 --num_buffers 50 --game [GAME_NAME] --batch_size 128 --data_dir_prefix [DIRECTORY_NAME] --model [MODEL_TYPE]
We also provide a script, tune_simclr.py that uses hyper-parameter search to find the optimal settings for the SimRCRL Loss.
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We build our gym implementation on top of the original decision transformer and benchmark our results on the following OpenAI Gym environments:
- Hopper
- HalfCheetah
- Walker2D
- Pen (Adroit Handgrip)
- Hammer (Adroit Handgrip)
- Relocate (Adroit Handgrip)
Experiments require MuJoCo. Follow the instructions in the mujoco-py repo to install. Then, dependencies can be installed with the following command:
conda env create -f conda_env_gpu.yml
Datasets are stored in the data directory.
Install the D4RL repo, following the instructions there.
Then, run the following script in order to download the datasets a``nd save them in our format:
python download_d4rl_datasets.py
python download_adroid_datasets.py
Experiments for ConTran can be reproduced with the following:
python experiment.py --env [ENV_NAME] --dataset [DATASET_TYPE] --model_type [MODEL_TYPE]
Adding -w True will log results to Weights and Biases.
We also provide a script, tune_simclr.py that uses hyper-parameter search to find the optimal settings for the SimRCRL Loss.
Note: The OpenAI Gym experiments rely upon using Mujoco-2.1.0, Gym==0.18.3, and d4rl==1.1. Using later versions of Gym may result in errors when downloading the datasets because the environment names have changed.
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