Example of using an inverse reinforcement learning algorithm such as GAIL to learn to imitate a PPO policy to play Cartpole. In this scenario we consider the PPO policy to be the expert and GAIL has to learn to imitate it only by observing expert's trajectories. Note that both policies succed to master the game by achieving an average score of at least 199 points out of 200 over 100 episodes.
Before start using this repository, install the required libraries in the requirements.txt file. You can also refere to the file notebook.ipynb to check the results on notebook.
pip install -r requirements.txt"
Train PPO, to play Cartpole and save its weights.
python PPO/main.py --train --save
Evaluate the performance of PPO (expert policy).
python PPO/main.py --load --eval
Collect 100 trajectories from PPO playing Cartpole. Since we want our imitating policy to learn only on trajectories of good quality, we are going to keep only those trajectories whose total final episode reward is >= 195.
python PPO/main.py --collect --min_reward 195 --n_traj 100
Train GAIL to imitate PPO.
python main.py --train --save --expert_trajectories_path 'PPO/trajectories/cart_pole.pickle'
Evaluate the performance of GAIL.
python main.py --load --eval
Visualize training details on Tensorboard.
tensorboard --logdir=runs
| PPO Reward vs Training Episodes |
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| GAIL Reward vs Training Episodes |
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