HumanCompatibleAI

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ppo-seals-CartPole-v0

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Center for Human-Compatible AI 15

Using the following code, stable-baselines3=2.3.2, imitation=1.0.0

import numpy as np
import gymnasium as gym
from stable_baselines3.common.evaluation import evaluate_policy

from imitation.algorithms import bc
from imitation.data import rollout
from imitation.data.wrappers import RolloutInfoWrapper
from imitation.policies.serialize import load_policy
from imitation.util.util import make_vec_env

rng = np.random.default_rng(0)
env = make_vec_env(
    "seals:seals/CartPole-v0",
    rng=rng,
    n_envs=1,
    post_wrappers=[lambda env, _: RolloutInfoWrapper(env)],  # for computing rollouts
)
expert = load_policy(
    "ppo-huggingface",
    organization="HumanCompatibleAI",
    env_name="seals-CartPole-v0",
    venv=env,
    # observation_space=env.observation_space,
    # action_space=env.action_space
)
rollouts = rollout.rollout(
    expert,
    env,
    rollout.make_sample_until(min_timesteps=None, min_episodes=50),
    rng=rng,
)
transitions = rollout.flatten_trajectories(rollouts)

bc_trainer = bc.BC(
    observation_space=env.observation_space,
    action_space=env.action_space,
    demonstrations=transitions,
    rng=rng,
)
bc_trainer.train(n_epochs=10)
# reward, _ = evaluate_policy(bc_trainer.policy, env, 10)
# print("Reward:", reward)