Efficient Task Adaptation by Mixing Discovered Skills;

This repository implements agents introduced in Efficient Task Adaptation by Mixing Discovered Skills paper.

Same importance agent : agents/diayn_same_weight.py Simple importance agent : agents/diayn_simple_weight.py DIAYN controller agent : agents/SaP.py Scratch controller agent : agents/SaP.py (run with 'init_diayn=false' option)

Note that we named our best performing agent DIAYN controller agent as SaP, which stands for 'Skill as persepective'. This codebase is built on top of the Unsupervised Reinforcement Learning Benchmark (URLB) codebase. We include agents for all baselines in the agents folder.

To obtain pre-trained weight, run the following command:

python pretrain.py agent=diayn domain=walker experiment=YOUR_EXP_NAME

After pretraining, to finetune your agent, run the following command.

python finetune.py agent=AGENT_NAME experiment=YOUR_EXP_NAME task=walker_stand

Make sure to specify the directory of your saved snapshots referring to .yaml file for each agent. For example, to run SaP agent, run below code.

# run SaP agent
python finetune.py agent=SaP experiment=YOUR_EXP_NAME task=walker_stand

Requirements

We assume you have access to a GPU that can run CUDA 10.2 and CUDNN 8. Then, the simplest way to install all required dependencies is to create an anaconda environment by running

conda env create -f conda_env.yml

After the instalation ends you can activate your environment with

conda activate urlb

Available Domains

We support the following domains.

Domain	Tasks
walker	stand, walk, run, flip
quadruped	walk, run, stand, jump
jaco	reach_top_left, reach_top_right, reach_bottom_left, reach_bottom_right