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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:
 Learning a Reward Function using Preference Comparisons
The preference comparisons algorithm learns a reward function by comparing trajectory segments to each other.
To set up the preference comparisons algorithm, we first need to set up a lot of its internals beforehand
Step1:  Then we can start training the reward model. Note that we need to specify the total timesteps that the agent should be trained and how many fragment comparisons should be made.
Step2:  After we trained the reward network using the preference comparisons algorithm, we can wrap our environment with that learned reward.
Step3:  Now we can train an agent, that only sees those learned reward.
Step4:  Then we can evaluate it using the original reward.
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Python Code:
from imitation.algorithms import preference_comparisons
from imitation.rewards.reward_nets import BasicRewardNet
from imitation.util.networks import RunningNorm
from imitation.policies.base import FeedForward32Policy, NormalizeFeaturesExtractor
import seals
import gym
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3 import PPO
venv = DummyVecEnv([lambda: gym.make("seals/CartPole-v0")] * 8)
reward_net = BasicRewardNet(
    venv.observation_space, venv.action_space, normalize_input_layer=RunningNorm
)
fragmenter = preference_comparisons.RandomFragmenter(warning_threshold=0, seed=0)
gatherer = preference_comparisons.SyntheticGatherer(seed=0)
reward_trainer = preference_comparisons.CrossEntropyRewardTrainer(
    model=reward_net,
    epochs=3,
)
agent = PPO(
    policy=FeedForward32Policy,
    policy_kwargs=dict(
        features_extractor_class=NormalizeFeaturesExtractor,
        features_extractor_kwargs=dict(normalize_class=RunningNorm),
    ),
    env=venv,
    seed=0,
    n_steps=2048 // venv.num_envs,
    batch_size=64,
    ent_coef=0.0,
    learning_rate=0.0003,
    n_epochs=10,
)
trajectory_generator = preference_comparisons.AgentTrainer(
    algorithm=agent,
    reward_fn=reward_net,
    exploration_frac=0.0,
    seed=0,
)
pref_comparisons = preference_comparisons.PreferenceComparisons(
    trajectory_generator,
    reward_net,
    fragmenter=fragmenter,
    preference_gatherer=gatherer,
    reward_trainer=reward_trainer,
    comparisons_per_iteration=100,
    fragment_length=100,
    transition_oversampling=1,
    initial_comparison_frac=0.1,
    allow_variable_horizon=False,
    seed=0,
    initial_epoch_multiplier=2,  # Note: set to 200 to achieve sensible results
)
Explanation: Learning a Reward Function using Preference Comparisons
The preference comparisons algorithm learns a reward function by comparing trajectory segments to each other.
To set up the preference comparisons algorithm, we first need to set up a lot of its internals beforehand:
End of explanation
pref_comparisons.train(
    total_timesteps=1000,  # Note: set to 40000 to achieve sensible results
    total_comparisons=120,  # Note: set to 4000 to achieve sensible results
)
Explanation: Then we can start training the reward model. Note that we need to specify the total timesteps that the agent should be trained and how many fragment comparisons should be made.
End of explanation
from imitation.rewards.reward_wrapper import RewardVecEnvWrapper
learned_reward_venv = RewardVecEnvWrapper(venv, reward_net.predict)
Explanation: After we trained the reward network using the preference comparisons algorithm, we can wrap our environment with that learned reward.
End of explanation
from stable_baselines3 import PPO
from stable_baselines3.ppo import MlpPolicy
learner = PPO(
    policy=MlpPolicy,
    env=learned_reward_venv,
    seed=0,
    batch_size=64,
    ent_coef=0.0,
    learning_rate=0.0003,
    n_epochs=10,
    n_steps=64,
)
learner.learn(1000)  # Note: set to 100000 to train a proficient expert
Explanation: Now we can train an agent, that only sees those learned reward.
End of explanation
from stable_baselines3.common.evaluation import evaluate_policy
reward, _ = evaluate_policy(agent.policy, venv, 10)
print(reward)
Explanation: Then we can evaluate it using the original reward.
End of explanation
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