


'''simple docstring'''







from __future__ import annotations




def 							__lowercase    (   __SCREAMING_SNAKE_CASE		)					->       List[Any]:
     """simple docstring"""




     for i in range(1					,	len(matrix[0]		)		):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1					,	len(UpperCamelCase__		)		):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1					,	len(UpperCamelCase__		)		):
          for j in range(1					,	len(matrix[0]		)		):
               matrix[i][j] += min(matrix[i - 1][j]					,	matrix[i][j - 1]		)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()









import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase						:  List[Any]									=			{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
					_UpperCAmelCase						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

					_UpperCAmelCase						:  Tuple									=			ValueGuidedRLPipeline.from_pretrained(
					    """bglick13/hopper-medium-v2-value-function-hor32""",
					    env=env,
					)

					env.seed(0)
					_UpperCAmelCase						:  Tuple									=			env.reset()
					_UpperCAmelCase						:  str									=			0
					_UpperCAmelCase						:  Union[str, Any]									=			0
					_UpperCAmelCase						:  List[str]									=			1000
					_UpperCAmelCase						:  Optional[int]									=			[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase						:  int									=			pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  Tuple									=			env.get_normalized_score(total_reward)

															# update return
															total_reward += reward
															total_score += score
															print(
															    F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
															    F''' {total_score}'''
															)

															# save observations for rendering
															rollout.append(next_observation.copy())

															_UpperCAmelCase						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

					print(F'''Total reward: {total_reward}''')




