Direct Behavior Specification via Constrained RL

Code to reproduce the Arena environment experiments from Direct Behavior Specification via Constrained Reinforcement Learning. See installation and run procedures below.

License

Please read the license. Here's a summary.

Installation

• Create a conda environment: conda create --name dbs python=3.8.8
• Install dependencies: pip install -r requirements.txt

To train a model

Simply run main.py with the desired arguments.

examples:

SAC with Reward Engineering

python main.py --constraints_to_enforce is-looking-at-marker is-in-lava is-above-energy-limit --constraint_is_reversed true false true --constraint_fixed_weights 0.25 2. 0.5 --constraint_discount_factors 0.9 0.9 0.9 --constraint_rates_to_add_as_obs is-looking-at-marker is-in-lava is-above-energy-limit --constraint_enforcement_method reward_engineering --steps_bw_update 200 --num_steps 5000000 --desc rewardEngineering

SAC-Lagrangian with single constraints

python main.py --constraints_to_enforce is-above-energy-limit --constraint_is_reversed true --constraint_enforcement_method lagrangian --constraint_thresholds nan-0.01 --constraint_discount_factors 0.9 --constraint_rates_to_add_as_obs is-above-energy-limit --num_steps 3000000 --desc singleConstraintEnergy

python main.py --constraints_to_enforce is-on-ground --constraint_is_reversed true --constraint_enforcement_method lagrangian --constraint_thresholds nan-0.40 --constraint_discount_factors 0.9 --constraint_rates_to_add_as_obs is-on-ground --num_steps 3000000 --desc singleConstraintJump

python main.py --constraints_to_enforce is-in-lava --constraint_is_reversed false --constraint_enforcement_method lagrangian --constraint_thresholds nan-0.01 --constraint_discount_factors 0.9 --constraint_rates_to_add_as_obs is-in-lava --num_steps 3000000 --desc singleConstraintLava

python main.py --constraints_to_enforce is-looking-at-marker --constraint_is_reversed true --constraint_enforcement_method lagrangian --constraint_thresholds nan-0.10 --constraint_discount_factors 0.9 --constraint_rates_to_add_as_obs is-looking-at-marker --num_steps 3000000 --desc singleConstraintLookat

python main.py --constraints_to_enforce is-above-speed-limit --constraint_is_reversed false --constraint_enforcement_method lagrangian --constraint_thresholds nan-0.01 --constraint_discount_factors 0.9 --constraint_rates_to_add_as_obs is-above-speed-limit --num_steps 3000000 --desc singleConstraintSpeed

SAC-Lagrangian with multiple constraints

python main.py --constraints_to_enforce has-reached-goal-in-episode is-looking-at-marker is-on-ground is-in-lava is-above-speed-limit is-above-energy-limit --constraint_is_reversed false true true false false true --constraint_thresholds 0.99-nan,nan-0.1,nan-0.4,nan-0.01,nan-0.01,nan-0.01 --constraint_discount_factors 0.9 0.9 0.9 0.9 0.9 0.9 --constraint_rates_to_add_as_obs is-looking-at-marker is-on-ground is-in-lava is-above-speed-limit is-above-energy-limit --bootstrap_constraint has-reached-goal-in-episode --constraint_enforcement_method lagrangian --num_steps 10000000 --desc allConstraints

To visualise a model

Simply run evaluate.py the appropriate arguments.

example:

python evaluate.py --root_dir storage --storage_name No4_sac_ArenaEnv-v0_singleConstraintLava --max_episode_len 100 --n_episodes 10 --render true

Bibtex

@article{roy2021direct,
  title={Direct Behavior Specification via Constrained Reinforcement Learning},
  author={Roy, Julien and Girgis, Roger and Romoff, Joshua and Bacon, Pierre-Luc and Pal, Christopher},
  journal={arXiv preprint arXiv:2112.12228},
  year={2021}
}

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