Skip to content

Latest commit

 

History

History
executable file
·
72 lines (58 loc) · 2.9 KB

README.md

File metadata and controls

executable file
·
72 lines (58 loc) · 2.9 KB
Raw

PTR Implementation

This repository contains the implementation of the PTR algorithm described in the paper: Pre-Training for Robots: Leveraging Diverse Multitask Data via Offline Reinforcement Learning.

Code Structure

The code is structured as follows:

  • data_preprocessing/generate_numpy.py: contains the code to preprocess the data.
  • jaxrl2/agents/cql_encoder_sep_parallel: contains our parallelized implementation of the PTR algorithm. This code is builds on the ideas introduced in the JAX TPU Colab. Note the parallelization environment is adaptive and does work with single GPU/CPU as well.
  • jaxrl2/utils: contains the code for the environment and dataset wrappers.
  • examples/configs: contains the config files for the dataset
  • examples/scripts: contains the script(s) to run the experiment

Sample Run Command

    XLA_PYTHON_CLIENT_PREALLOCATE=false python3 examples/launch_train_real_cql.py 
    --prefix resnet34_all_pp_data \
    --cql_alpha 10 \
    --encoder_type $encoder_type \
    --algorithm cql_encodersep_parallel \
    --dataset $data \ 
    --target_dataset $tdata \
    --batch_size 64 \
    --wandb_project $proj_name \
    --multi_viewpoint 0 \
    --add_prev_actions 0 \
    --policy_encoder_type same \
    --target_mixing_ratio 0.9 \
    --use_action_sep 1 \
    --use_basis_projection 0 \
    --discount 0.96 \
    --max_q_backup 1 \
    --num_final_reward_steps 3 \
    --term_as_rew 1 \
    --encoder_norm group \
    --use_spatial_learned_embeddings 1  \
    --target_entropy_factor 1.0 \
    --use_pixel_sep $pixel \
    --min_q_version 3 \
    --q_dropout_rate $drop \ 
    --offline_finetuning_start 160000

Installation

Directly from source

You can install the package by running the following command:

pip install --upgrade pip
python setup.py develop

pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

See instructions for other versions of CUDA here.

Conda Environment Setup

Additionally, you can create a conda environment for a TPU machine with the required dependencies:

conda env create -f jax_tpu.yml
conda activate jax_tpu

The jax_tpu.yml file is located in the root directory of this repository.

Public Datasets

You can find the datasets that were used for this paper here.

Acknowledgements

Our repostiory is based off of the JAX RL2 repository. We thank the authors for making their code public. We utilized on a earlier private version of the repository for our experiments. We have made the necessary changes to make it compatible with the latest version of the repository.