Offline RL Algorithms

This repository aims to provide a simple PyTorch implementation of state-of-the-art Offline RL methods. Some of the methods like AWAC, CQL have been tested on MuJoCo locomotion tasks based on d4rl dataset.

Installation

• Mujoco
• PyTorch
• d4rl(https://github.com/rail-berkeley/d4rl)
• gym

As an alternative, to replicate the environment used for running the code (might contain a lot of unnecessary libraries as well):

pip install environment/requirements.txt 

or via conda

cd environment
conda create --name <env> --file environment/requirements_conda.txt

Running the code

python run_agent.py --env <env_name>  --seed <seed_no>  --exp_name <experiment name> --algorithm <offline rl algorithm>

List of Implemented Algorithms

• 'SAC' : Soft Actor Critic
• 'CQL-rho-fixed' : Conservative Q learning rho-version fixed alpha
• 'CQL-rho-lagrange' : Conservative Q learning rho-version trained alpha
• 'CQL-H-fixed' : Conservative Q learning H-version fixed alpha
• 'CQL-H-lagrange' : Conservative Q learning H-version trained alpha
• 'CWR-exp' : Critic weighted regression (exponential filtering) or AWAC (Advantage weighted actor critic)
• 'CWR-binary': Critic weighted regression (binary filtering)
• 'CWR-binary-max': Critic weighted regression (binary filtering with pessimistic advantage estimates)
• 'EMAQ': Expected Max Q learning operator

Plotting

python plot.py <data_folder> --value <y_axis_coordinate> 

The plotting script will plot all the subfolders inside the given folder. The value is the y-axis that is required. 'value' can be:

• AverageTestEpRet