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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Train an Agent using Adversarial Inverse Reinforcement Learning
As usual, we first need an expert. 
Note that we now use a variant of the CartPole environment from the seals package, which has fixed episode durations. Read more about why we do this here.
Step1:  We generate some expert trajectories, that the discriminator needs to distinguish from the learner's trajectories.
Step2:  Now we are ready to set up our AIRL trainer.
Note, that the reward_net is actually the network of the discriminator.
We evaluate the learner before and after training so we can see if it made any progress.
Step3:  When we look at the histograms of rewards before and after learning, we can see that the learner is not perfect yet, but it made some progress at least.
If not, just re-run the above cell.
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Python Code:
from stable_baselines3 import PPO
from stable_baselines3.ppo import MlpPolicy
import gym
import seals
env = gym.make("seals/CartPole-v0")
expert = PPO(
    policy=MlpPolicy,
    env=env,
    seed=0,
    batch_size=64,
    ent_coef=0.0,
    learning_rate=0.0003,
    n_epochs=10,
    n_steps=64,
)
expert.learn(1000)  # Note: set to 100000 to train a proficient expert
Explanation: Train an Agent using Adversarial Inverse Reinforcement Learning
As usual, we first need an expert. 
Note that we now use a variant of the CartPole environment from the seals package, which has fixed episode durations. Read more about why we do this here.
End of explanation
from imitation.data import rollout
from imitation.data.wrappers import RolloutInfoWrapper
from stable_baselines3.common.vec_env import DummyVecEnv
rollouts = rollout.rollout(
    expert,
    DummyVecEnv([lambda: RolloutInfoWrapper(gym.make("seals/CartPole-v0"))] * 5),
    rollout.make_sample_until(min_timesteps=None, min_episodes=60),
)
Explanation: We generate some expert trajectories, that the discriminator needs to distinguish from the learner's trajectories.
End of explanation
from imitation.algorithms.adversarial.airl import AIRL
from imitation.rewards.reward_nets import BasicShapedRewardNet
from imitation.util.networks import RunningNorm
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv
import gym
import seals
venv = DummyVecEnv([lambda: gym.make("seals/CartPole-v0")] * 8)
learner = PPO(
    env=venv,
    policy=MlpPolicy,
    batch_size=64,
    ent_coef=0.0,
    learning_rate=0.0003,
    n_epochs=10,
)
reward_net = BasicShapedRewardNet(
    venv.observation_space, venv.action_space, normalize_input_layer=RunningNorm
)
airl_trainer = AIRL(
    demonstrations=rollouts,
    demo_batch_size=1024,
    gen_replay_buffer_capacity=2048,
    n_disc_updates_per_round=4,
    venv=venv,
    gen_algo=learner,
    reward_net=reward_net,
)
learner_rewards_before_training, _ = evaluate_policy(
    learner, venv, 100, return_episode_rewards=True
)
airl_trainer.train(20000)  # Note: set to 300000 for better results
learner_rewards_after_training, _ = evaluate_policy(
    learner, venv, 100, return_episode_rewards=True
)
Explanation: Now we are ready to set up our AIRL trainer.
Note, that the reward_net is actually the network of the discriminator.
We evaluate the learner before and after training so we can see if it made any progress.
End of explanation
import matplotlib.pyplot as plt
import numpy as np
print(np.mean(learner_rewards_after_training))
print(np.mean(learner_rewards_before_training))
plt.hist(
    [learner_rewards_before_training, learner_rewards_after_training],
    label=["untrained", "trained"],
)
plt.legend()
plt.show()
Explanation: When we look at the histograms of rewards before and after learning, we can see that the learner is not perfect yet, but it made some progress at least.
If not, just re-run the above cell.
End of explanation
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