Awesome

ActiveRL

This is a repository containing the code that was used to run the experiments for the paper Active Reinforcement Learning for Robust Building Control. It contains code for experiments with the Sinergym building simulation environment, and for future work involving the CityLearn, dm_control, and simplegrid Gym environments. Only the Sinergym integration is fully complete as of the writing of this README.

The ActivePLR algorithm is designed in such a way that although its principles are general, its actual implementation will be very environment specific. As such, <b> this repo is not intended to be able to be used out-of-the-box to implement ActivePLR</b>, unless you are seeking to expand specifically on ActivePLR's application to Sinergym. Instead, this repo should be taken as more of a reference.

Important Files

The bulk of the implementation of ActivePLR is spread across files in the core/ directory.

root
└── core/
    ├── uncertain_ppo/
    ├── callbacks.py
    ├── resettable_env.py
    ├── state_generation.py
    └── ... other experimental and util files

The uncertain_ppo/ directory holds an implementation of PPO that trains via Monte Carlo Dropout and is able to estimate its critic's uncertainty at a given state.
The resettable_env.py file defines the interface all Gym environments should adhere to in order to be compatible with the ActivePLR algorithm.
The state_generation.py file implements the step in ActivePLR where we generate a new environment configuration, given an environment and a policy.
The callbacks.py file brings all the pieces together in the form of callbacks that are executed at different times of the RLLib PPO training process, to implement ActivePLR.

Outside of the core/ directory are integrations with various Gym environments. Of special note is the sinergym_wrappers/sinergym_wrapper.py file, in which we have tweaked the reset function in order to be able to tweak more environment configuration parameters than just the temperature.

Setup

Instructions for general machines:

1. Clone repo
5. Navigate to wherever you cloned the repo
6. conda env create -f environment.yml
7. conda activate ActiveRL
8. pip install git+https://github.com/cooper-org/cooper.git
9. pip install -e gridworld/gym-simplegrid/ --no-deps
10. pip install moviepy==1.0.3
11. pip uninstall pygame
12. Install bcvtb and EnergyPlus like in step 2 and 3 of https://github.com/ugr-sail/sinergym
12. pip install sinergym[extras]
13. pip install gym==0.24.1
14. pip install -e gym-simplegrid
15. pip install dm_control==1.0.9

Instructions for the Savio compute cluster:

1. Clone repo
2. cd /global/home/users/$USER/
3. mv .conda /global/scratch/users/$USER/.conda
4. ln -s /global/scratch/users/$USER/.conda .conda
5. Navigate to wherever you cloned the repo
6. conda install -f environment.yml
7. conda activate ActiveRL
8. pip install git+https://github.com/cooper-org/cooper.git
9. pip install -e gym-simplegrid/ --no-deps
10. pip install moviepy==1.0.3
11. pip uninstall pygame
12. pip install sinergym[extras]
13. pip install gym==0.24.1

Running with SLURM

To run a wandb sweep on a cluster using SLURM: sbatch_scripts//master_sbatch_script.sh WANDB_SWEEP_ID NUMBER_OF_RUNS

To run an individual run on a cluster using SLURM: sbatch_scripts//master_sbatch_script_nonsweep.sh "COMMAND LINE ARGS NOT INCLUDING THE ACTUAL PYTHON COMMAND" NUMBER_OF_RUNS For example: sbatch_scripts//master_sbatch_script_nonsweep.sh "run_experiments.py --num_timesteps=400000 --gw_steps_per_cell=10 --wandb --env=gw --no_coop --gw_filename=gridworlds/good_bubble.txt --num_descent_steps=10 --seed=1234567 --use_activerl=1" 1

If running in a singularity container, use sbatch_scripts/master_singularity_sbatch_script.sh and sbatch_scripts/master_singularity_sbatch_nonsweep.sh instead

Commands for Reproducibility

Here are the individual commands that were used to generate data for the paper:

Seeds: 8765, 87654, 876543, 8765432, 87654321

ActiveRL: run_experiments.py --num_timesteps=7500000 --wandb --env=sg --num_gpus=1 --train_batch_size=26280 --horizon=4380 --num_training_workers=3 --num_eval_workers=3 --eval_interval=3 --num_envs_per_worker=2 --num_descent_steps=20 --clip_param=0.3 --gamma=0.8 --lr=5e-05 --num_sgd_iter=40 --dropout=0.1 --continuous --activerl_lr=0.01 --activerl_reg_coeff=0.5 --dropout=0.1 --num_descent_steps=91 --num_dropout_evals=10 --only_vary_offset --seed=SEED --sinergym_sweep=1.0,0,0,0

RBC: run_experiments.py --num_timesteps=7500000 --wandb --env=sg --num_gpus=1 --train_batch_size=26280 --horizon=4380 --num_training_workers=3 --num_eval_workers=3 --eval_interval=3 --num_envs_per_worker=2 --num_descent_steps=20 --clip_param=0.3 --gamma=0.8 --lr=5e-05 --num_sgd_iter=40 --dropout=0.1 --continuous --activerl_lr=0.01 --activerl_reg_coeff=0.5 --dropout=0.1 --num_descent_steps=91 --num_dropout_evals=10 --only_vary_offset --seed=SEED --sinergym_sweep=0,1,0,0

RL: run_experiments.py --num_timesteps=7500000 --wandb --env=sg --num_gpus=1 --train_batch_size=26280 --horizon=4380 --num_training_workers=3 --num_eval_workers=3 --eval_interval=3 --num_envs_per_worker=2 --num_descent_steps=20 --clip_param=0.3 --gamma=0.8 --lr=5e-05 --num_sgd_iter=40 --dropout=0.1 --continuous --activerl_lr=0.01 --activerl_reg_coeff=0.5 --dropout=0.1 --num_descent_steps=91 --num_dropout_evals=10 --only_vary_offset --seed=SEED --sinergym_sweep=0,0,0,0

Domain Randomization: run_experiments.py --num_timesteps=7500000 --wandb --env=sg --num_gpus=1 --train_batch_size=26280 --horizon=4380 --num_training_workers=3 --num_eval_workers=3 --eval_interval=3 --num_envs_per_worker=2 --num_descent_steps=20 --clip_param=0.3 --gamma=0.8 --lr=5e-05 --num_sgd_iter=40 --dropout=0.1 --continuous --activerl_lr=0.01 --activerl_reg_coeff=0.5 --dropout=0.1 --num_descent_steps=91 --num_dropout_evals=10 --only_vary_offset --seed=SEED --sinergym_sweep=0,0,0,1.0

ActivePLR: run_experiments.py --num_timesteps=7500000 --wandb --env=sg --num_gpus=1 --train_batch_size=26280 --horizon=4380 --num_training_workers=3 --num_eval_workers=3 --eval_interval=3 --num_envs_per_worker=2 --num_descent_steps=20 --clip_param=0.3 --gamma=0.8 --lr=5e-05 --num_sgd_iter=40 --dropout=0.1 --continuous --activerl_lr=0.01 --activerl_reg_coeff=0.5 --dropout=0.1 --num_descent_steps=91 --num_dropout_evals=10 --only_vary_offset --plr_d=1 --seed=SEED --sinergym_sweep=1.0,0,0,0

Active RPLR: run_experiments.py --num_timesteps=7500000 --wandb --env=sg --num_gpus=1 --train_batch_size=26280 --horizon=4380 --num_training_workers=3 --num_eval_workers=3 --eval_interval=3 --num_envs_per_worker=2 --num_descent_steps=20 --clip_param=0.3 --gamma=0.8 --lr=5e-05 --num_sgd_iter=40 --dropout=0.1 --continuous --activerl_lr=0.01 --activerl_reg_coeff=0.5 --dropout=0.1 --num_descent_steps=91 --num_dropout_evals=10 --only_vary_offset --plr_d=1 --plr_robust --seed=SEED --sinergym_sweep=1.0,0,0,0

RPLR: run_experiments.py --num_timesteps=3000000 --wandb --env=sg --num_gpus=1 --train_batch_size=26280 --horizon=4380 --num_training_workers=3 --num_eval_workers=3 --eval_interval=3 --num_envs_per_worker=2 --num_descent_steps=20 --clip_param=0.3 --gamma=0.8 --lr=5e-05 --num_sgd_iter=40 --dropout=0.1 --continuous --activerl_lr=0.01 --activerl_reg_coeff=0.5 --dropout=0.1 --num_descent_steps=91 --num_dropout_evals=10 --only_vary_offset --plr_d=1 --plr_robust --plr_beta=0.03362866617598082 --plr_envs_to_1=50 --plr_rho=0.0064809998847552355 --seed=8765 --sinergym_sweep=0,0,0,1.0

Citation

@article{jang2023active,
  title={Active Reinforcement Learning for Robust Building Control},
  author={Jang, Doseok and Yan, Larry and Spangher, Lucas and Spanos, Costas},
  journal={arXiv preprint arXiv:2312.10289},
  year={2023}
}