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Imitation Learning Framework

This repository contains the code for the paper "Imitation learning by state-only distribution matching" by Damian Boborzi, Christoph-Nikolas Straehle, Jens S. Buchner, and Lars Mikelsons.

installation

conda environment config is in ./conda-spec-file.txt pip installations are in ./requirements.txt

Following repositories are important: Adapted FrEIA: https://github.com/FeMa42/FrEIA-NF-policy/tree/NF-Policy

To install pybullet use: pip install pybullet

Note that SOIL-TDM, FORM and the Normalizing Flow policy all use the FrEIA repository for the implementation of the Normalizing Flows. The FrEIA repository is therefor neccesary for these methods to work. Also note that (at this moment) the Normalizing Flow implementations are only compatible with CUDA-GPU and CPU versions of torch (tested under Linux Ubuntu 20.04). The Metal-GPU (for Apple M1 and M2 GPUs) implementation of torch is not yet compatible with the Normalizing Flows based on the FrEIA repository. As a fallback for Mac systems you can use the the cpu version of torch (this is slow). Using A MLP as policy with DAC and BC is also possible with the metal-GPU version of torch.

Implementation of BC, DAC, FORM, SOIL-TDM

Contains Implementation for DAC, FORM and SOIL-TDM all using SAC as the RL-Optmizer.

Training and Pretraining with behavioral cloning is also possible. It is possible to use a Normalizing Flow policy with DAC and BC instead of a conditional Gaussian policy.

Starting Expert Policy Training

Start file: gym_exp_data.py

Examples for training Expert policies using SAC:

python gym_exp_data.py --max-frames 100000 --exp-dir pendulum_SAC_MLP --env-name Pendulum-v1 --num-envs 16 --use-mlp --hidden-size 128 --num-env-steps 10 --internal-update-epochs 10 --replay-buffer-size 100000
python gym_exp_data.py --max-frames 400000 --exp-dir cheetah_SAC_MLP --env-name HalfCheetahBulletEnv-v0 --num-envs 16 --hidden-size 512 --lr-p 3e-4 --lr-q 5e-4 --mini-batch-size 512 --num-env-steps 10 --internal-update-epochs 10 --polyak 0.99 --use-mlp
python gym_exp_data.py --max-frames 400000 --exp-dir hopper_SAC_MLP --env-name HopperBulletEnv-v0 --num-envs 16 --hidden-size 512 --lr-p 3e-4 --lr-q 5e-4 --mini-batch-size 512 --num-env-steps 10 --internal-update-epochs 10 --polyak 0.99 --use-mlp
python gym_exp_data.py --max-frames 600000 --exp-dir humanoid_SAC_MLP --env-name HumanoidBulletEnv-v0 --num-envs 16 --hidden-size 512 --lr-p 3e-4 --lr-q 5e-4 --mini-batch-size 512 --num-env-steps 10 --internal-update-epochs 10 --polyak 0.99 --use-mlp
python gym_exp_data.py --max-frames 600000 --exp-dir walker2D_SAC_MLP --env-name Walker2DBulletEnv-v0 --num-envs 16 --hidden-size 512 --lr-p 3e-4 --lr-q 5e-4 --mini-batch-size 512 --num-env-steps 10 --internal-update-epochs 10 --polyak 0.99 --use-mlp
python gym_exp_data.py --max-frames 600000 --exp-dir ant_SAC_MLP --env-name AntBulletEnv-v0 --num-envs 16 --hidden-size 512 --lr-p 3e-4 --lr-q 5e-4 --mini-batch-size 512 --num-env-steps 10 --internal-update-epochs 10 --polyak 0.99 --use-mlp
python gym_exp_data.py --max-frames 100000 --exp-dir cartpole_SAC_MLP --env-name CartPoleContinuousBulletEnv-v0 --num-envs 16 --hidden-size 512 --lr-p 3e-4 --lr-q 5e-4 --mini-batch-size 512 --num-env-steps 10 --internal-update-epochs 10 --polyak 0.99 --use-mlp

Starting Imitation Learning Routines

Start file: flow_rl.py.

It saves the parameters and logs as well as tensorboard in the output directory under "Experiments/..." which has to be provided.

Example for Imitation Learning using SOIL-TDM:

python flow_rl.py --exp-name cheetah_soil_tdm_n4 --env-name HalfCheetahBulletEnv-v0 --n-trajectories 4 --overwrite --use-mlp --total-epochs 400 --use-noise-sched --noise-red-factor 1.0 --expert-noise-value 0.05 --final-noise-value 0.02 --n-state-est-train 1000 --num-envs 16 --alpha-nonpolicy-rampup 150

Example for Imitation Learning using Form:

python flow_rl.py --exp-name hopper_form_n4 --env-name HopperBulletEnv-v0 --il-method "form" --n-trajectories 4 --overwrite --use-mlp --total-epochs 400 --use-noise-sched --noise-red-factor 1.0 --expert-noise-value 0.05 --final-noise-value 0.02 --n-state-est-train 1000 --num-envs 16 --automatic-entropy-tuning

Example for Imitation Learning using DAC:

python flow_rl.py --exp-name cheetah_dac_n4 --il-method dac --env-name HalfCheetahBulletEnv-v0 --num-envs 16 --n-trajectories 4 --overwrite --use-mlp --total-epochs 400

Example for Imitation Learning using BC:

python flow_rl.py --exp-name cheetah_bc_n4 --il-method bc --env-name HalfCheetahBulletEnv-v0 --num-envs 2 --n-trajectories 4 --overwrite --use-mlp --total-epochs 0 --num-bc-updates 10000

Normalizing Flow Policy

Example for Imitation Learning with Normalizing Flow policy using BC:

python flow_rl.py --exp-name cheetah_bc_nfpolicy_n10 --il-method bc --env-name HalfCheetahBulletEnv-v0 --num-envs 2 --n-trajectories 10 --overwrite --total-epochs 0 --num-bc-updates 10000

Example for Imitation Learning with Normalizing Flow policy using DAC:

python flow_rl.py --exp-name cheetah_dac_nfpolicy_n10 --il-method dac --env-name HalfCheetahBulletEnv-v0 --num-envs 16 --n-trajectories 10 --overwrite --use-mlp --total-epochs 400

You can also train an expert model with a Normalizing Flow policy:

python gym_exp_data.py --max-frames 400000 --exp-dir cheetah_SAC_nfpolicy --env-name HalfCheetahBulletEnv-v0 --num-envs 16 --hidden-size 512 --lr-p 3e-4 --lr-q 5e-4 --mini-batch-size 512 --num-env-steps 10 --internal-update-epochs 10 --polyak 0.99

``