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- # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
- import parl
- import numpy as np
- from osim.env import L2M2019Env
- from env_wrapper import FrameSkip, ActionScale, OfficialObs, FinalReward, FirstTarget
-
-
- @parl.remote_class
- class Actor(object):
- def __init__(self,
- difficulty,
- vel_penalty_coeff,
- muscle_penalty_coeff,
- penalty_coeff,
- only_first_target=False):
-
- random_seed = np.random.randint(int(1e9))
-
- env = L2M2019Env(
- difficulty=difficulty, visualize=False, seed=random_seed)
- max_timelimit = env.time_limit
-
- env = FinalReward(
- env,
- max_timelimit=max_timelimit,
- vel_penalty_coeff=vel_penalty_coeff,
- muscle_penalty_coeff=muscle_penalty_coeff,
- penalty_coeff=penalty_coeff)
-
- if only_first_target:
- assert difficulty == 3, "argument `only_first_target` is available only in `difficulty=3`."
- env = FirstTarget(env)
-
- env = FrameSkip(env)
- env = ActionScale(env)
- self.env = OfficialObs(env, max_timelimit=max_timelimit)
-
- def reset(self):
- observation = self.env.reset(project=False)
- return observation
-
- def step(self, action):
- return self.env.step(action, project=False)
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