This repository contains a Pytorch implementation of Generalized Advantage Estimation (GAE) and Generative Adversarial Imitation Learning (GAIL) with Proximal Policy Optimization (PPO)
For GAE, use
python gae.py --env-name Hopper-v1
For GAIL, use
python gail.py --env-name Hopper-v1 --expert-path hopper_expert_trajectories/ --batch-size 20000 --num-expert-trajs 10 --optim-epochs 5 --num-episodes 2000
For GAIL with Phase MLP architecture, use
python phase_gail.py --env-name Hopper-v1 --expert-path hopper_expert_trajectories/ --batch-size 20000 --num-expert-trajs 10 --optim-epochs 5 --num-episodes 2000