REWARD-INDUCED REPRESENTATION LEARNING

The implementation code of paper, based on the starter repo provided by CLVR.

1.OVERVIEW

• Agent(●) Following Target(■) RL task

  • 0 distractor(▲)
  
  • 1 distractor(▲)
  

• introduction

• reward-induced representation model

2.Get Start

• initialize a virtual environment

conda create --name myenv python=3.8
conda activate myenv

• configure environment dependencies

pip install -r requirements.txt

3.Implementation Tasks

1. re-implement the reward-induced representation learning model

MODEL = Encoder + MLPs + LSTM + rewards_heads(MLPs)

2. re-plicate the experiment to show the representation ability of model

EXP = Encoder(reward-induced) + Detached Decoder

3. implement PPO to finished the downstream task (agent following target) with reward-induced representation

4. build several representation models as baselines to train downstream task with PPO

(cnn|image-scratch|image-reconstruction|image-reconstruction-finetune|reward-prediction|reward-prediction-fintune|oracle)

5. train all of the above representation models and verify the better performance of reward-induced model

4. Files Summary

• /presentation : the presentation slides of the implementation task

• /re_implement_paper : the notes about the paper

• /scripts : the shell files to run the tasks

• /sprites_datagen : the dataset

• /sprites_env : the data environment

• /src : the src for README

• /tmp : the visualization results during training process

• /weights : the weights of models

• baseline.py : the baseline models for final training

• general_utils.py : general tool function

• model.py : the pre-trained representation learning models

• ppo_train.py : train the whole task with PPO

• ppo.py : PPO implementation

• pre_train.py : pre-train the representation learning models

• README.md : the project info

• requirement.txt : environmental dependencies

5. Run

1. provide a shell file to each task in /scripts

• pre-train representation models
  • pretrain_image_recon_decoder.sh
  • pretrain_image_recon_model.sh
  • pretrain_reward_pred_model.sh
• train the downsteam task with ppo
  • ppotrain_cnn.sh
  • ppotrain_image_rec_finetune.sh
  • ppotrain_image_rec.sh
  • ppotrain_image_scratch.sh
  • ppotrain_oracle.sh
  • ppotrain_reward_pred_finetune.sh
  • ppotrain_reward_pred.sh

2. change the parameters in each shell file like whether using gpus(gpus_num) or whether using wandb to record(is_use_wandb)