DesikRengarajan / FEDORA

[FL-ICML 2023] Code for Federated Ensemble-Directed Offline Reinforcement Learning

Federated Ensemble-Directed Offline Reinforcement Learning

Accepted at the Workshop of Federated Learning and Analytics in Practice, colocated with the 40th International Conference on Machine Learning (FL-ICML 2023)

Video of real world demonstration on TurtleBot

This codebase is based on the following publicly available git repositories:

• TD3-BC: sfujim/TD3_BC
• Flower: adap/flower
• Structure of simulation: CharlieDinh/pFedMe

The Python packages required to train FEDORA are listed in requirements.txt

Our experiments are performed on Python 3.8 in a Ubuntu Linux environment.

Federated learning with Flower

Directory: fed_flwr/

Specify federation parameters in config/s_config.yml amd config/c_config.yml Specify client learning parameters in config/c_config.yml

a. Launch server and clients individually

1. Launch server  
    python rl_server.py
    
2. Launch client (repeat for each client)
    python rl_client.py --gpu-index --eval-env --start-index --stop-index

where  
    gpu-index: index of CUDA device for PyTorch training  
    eval-env: name of the D4RL data-set source for this client  
    start-index: index of D4RL data-set to begin gathering data at  
    stop-index: index of D4RL data-set to stop gathering data at

b. To simplify this process, we share an example shell script with defalt parameters.

1. Specify client arguments in run_FEDORA.sh
2. Launch the shell script
    bash run_FEDORA.sh

The tensorboard logs will be saved in a folder called 'Results'

Federated learning simulation

Run single-threaded simulation of FEDORA. Helpful in training with limited computing resoures.

Directory: fed_sim/

Launch main program (for default parameters, simply execute python rl_main.py)

python rl_main.py --env-1 --env-2 --gpu-index-1 --gpu-index-2 --n-clients --ncpr \
    --n-rounds --dataset-size --seed --batch-size --alpha-0 --alpha-1 --alpha-2 \
    --local-epochs --temp-a --temp-a --decay-rate

where  
    env-1: name of the D4RL data-set source for first half clients  
    env-2: name of the D4RL data-set source for second half clients    
    gpu-index-1: index of CUDA device for training first half clients  
    gpu-index-2: index of CUDA device for training first second clients  
    n-clients: total number of clients  
    ncpr: number of clients participating in a round of federation  
    n-rounds: total rounds of federation  
    dataset-size: size of a client's data-set
    seed: random number seed  
and the others are FEDORA hyperparameters.

The tensorboard logs will be saved in a folder called 'Results'