Deep Inverse Reinforcement Learning

Contents

Chainer implementation of Adversarial Inverse Reinforcement Learning (AIRL) and Generative Adversarial Imitation Learning (GAIL). The code heavily depend on the reinforcement learning package Chainerrl.

Commands

Train and sample expert trajectory

python train_gym.py ppo --gpu $gpu_id --env CartPole-v0 --arch FFSoftmax --steps 50000 

Run GAIL

python train_gym.py gail --gpu $gpu_id --env CartPole-v0 --arch FFSoftmax --steps 100000 \
                    --load_demo ${PathOfDemonstrationNpzFile} --update-interval 128 --entropy-coef 0.01

Run AIRL

python train_gym.py airl --gpu $gpu_id --env CartPole-v0 --arch FFSoftmax --steps 100000 \
                    --load_demo ${PathOfDemonstrationNpzFile} --update-interval 128 --entropy-coef 0.01

LICENSE

MIT