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RRL: Resnet as representation for Reinforcement Learning

Resnet as representation for Reinforcement Learning (RRL) is a simple yet effective approach for training behaviors directly from visual inputs. We demonstrate that features learned by standard image classification models are general towards different task, robust to visual distractors, and when used in conjunction with standard Imitation Learning or Reinforcement Learning pipelines can efficiently acquire behaviors directly from proprioceptive inputs.

Final Behaviors acquired using RRL on ADROIT benchmark tasks (left to right) (a) Opening a door (b) Hammering a nail (c) Pen-twirling (d)) Object relocation

Setup

RRL codebase can be installed by cloning this repository. Note that it uses git submodules to resolve dependencies. Please follow the steps as below to install correctly.

1. Clone this repository along with the submodules

   git clone --recursive https://github.com/facebookresearch/RRL.git
   

2. Install the package using conda. The dependencies (apart from mujoco_py) are listed in env.yml

   conda env create -f env.yml
   
   conda activate rrl
   

3. The environment require MuJoCo as a dependency. You may need to obtain a license and follow the setup instructions for mujoco_py. Setting up mujoco_py with GPU support is highly recommended.

4. Install mj_envs and mjrl repositories.

   cd RRL
   pip install -e mjrl/.
   pip install -e mj_envs/.
   pip install -e .
   

5. Additionally, it requires the demonstrations published by hand_dapg

Running Instructions

1. First step is to convert the observations of demonstrations provided by hand_dapg to the encoder feature space. An example script is provided here. Note the script saves the demonstrations in a .pickle format inside the rrl/demonstrations directory.

   For the mj_envs tasks :

   python convertDemos.py --env_name hammer-v0 --encoder_type resnet34 -c top -d 
        
   
        

   python convertDemos.py --env_name door-v0 --encoder_type resnet34 -c top -d 
        
   
        

   python convertDemos.py --env_name pen-v0 --encoder_type resnet34 -c vil_camera -d 
        
   
        

   python convertDemos.py --env_name relocate-v0 --encoder_type resnet34 -c cam1 -c cam2 -c cam3 -d 
        
   
        

2. Launching RRL experiments using DAPG.

   An example launching script is provided job_script.py in the examples/ directory and the configs used are stored in the examples/config/ directory. Note : Hydra configs are used.

   python job_script.py  demo_file=
        
          --config-name hammer_dapg
   
        

   python job_script.py  demo_file=
        
          --config-name door_dapg
   
        

   python job_script.py  demo_file=
        
          --config-name pen_dapg
   
        

   python job_script.py  demo_file=
        
          --config-name relocate_dapg