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enjoy.py
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enjoy.py
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from argparse import ArgumentParser
import gym
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
np.set_printoptions(formatter={'float': "{:0.3f}".format})
class EnvFactory:
def __init__(self, env_name):
self.env_name = env_name
def make_env(self):
return gym.make(self.env_name, render=True)
def main(args):
expert = None
expert_state_dim = 0
if args.policy_path is not None:
policy_path = args.policy_path
expert = PPO.load(policy_path)
expert_state_dim = expert.observation_space.shape[0]
factory = EnvFactory(args.env)
env = DummyVecEnv([factory.make_env])
if args.stats_path is not None:
env = VecNormalize.load(args.stats_path, env)
env.training = False
else:
env = VecNormalize(env, training=False)
obs = env.reset()
env.render()
total_reward = 0
while True:
if expert is None:
action = env.action_space.sample()
action = np.zeros_like(action)
else:
good_obs = obs[:, :expert_state_dim]
action, _ = expert.predict(good_obs, deterministic=True)
obs, reward, done, info = env.step(action)
env.render()
reward = env.get_original_reward()
total_reward += reward[0]
if done:
print("Total reward: {:.3f}".format(total_reward))
obs = env.reset()
total_reward = 0
# Uncomment below to slow down rendering
# import time; time.sleep(0.05)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--env", help="Name of the environment as defined in __init__.py somewhere", type=str, required=True)
parser.add_argument("--policy_path", help="Path to policy zip file, if any. Otherwise compute null actions.", type=str)
parser.add_argument("--stats_path", help="Path to policy normalization stats.", type=str)
args = parser.parse_args()
main(args)