/dports/math/py-gym/gym-0.21.0/gym/utils/ |
H A D | env_checker.py | 54 action = env.action_space.sample() 146 if np.any(np.equal(action_space.low, action_space.high)): 148 if np.any(np.greater(action_space.low, action_space.high)): 150 if action_space.low.shape != action_space.shape: 152 if action_space.high.shape != action_space.shape: 158 np.any(np.abs(action_space.low) != np.abs(action_space.high)) 159 or np.any(np.abs(action_space.low) > 1) 190 action = action_space.sample() 305 action_space = env.action_space 307 env.step(env.action_space.sample()) [all …]
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/dports/math/py-gym/gym-0.21.0/gym/wrappers/ |
H A D | rescale_action.py | 18 env.action_space, spaces.Box 19 ), "expected Box action space, got {}".format(type(env.action_space)) 24 np.zeros(env.action_space.shape, dtype=env.action_space.dtype) + min_action 27 np.zeros(env.action_space.shape, dtype=env.action_space.dtype) + max_action 29 self.action_space = spaces.Box( 32 shape=env.action_space.shape, 33 dtype=env.action_space.dtype, 42 low = self.env.action_space.low 43 high = self.env.action_space.high
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H A D | clip_action.py | 10 assert isinstance(env.action_space, Box) 14 return np.clip(action, self.action_space.low, self.action_space.high)
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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/reinforcement-learning/ddpg/ |
H A D | strategies.py | 46 self.action_space = env_spec.action_space 47 self.state = np.ones(self.action_space.flat_dim) * self.mu 59 self.state = np.ones(self.action_space.flat_dim) * self.mu 69 self.action_space.low, 70 self.action_space.high) 78 self.action_space = Env2()
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/reinforcement-learning/ddpg/ |
H A D | strategies.py | 46 self.action_space = env_spec.action_space 47 self.state = np.ones(self.action_space.flat_dim) * self.mu 59 self.state = np.ones(self.action_space.flat_dim) * self.mu 69 self.action_space.low, 70 self.action_space.high) 78 self.action_space = Env2()
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/dports/devel/py-bullet3/bullet3-3.21/examples/pybullet/gym/pybullet_envs/minitaur/agents/tools/ |
H A D | wrappers_test.py | 46 env.step(env.action_space.sample()) 47 env.step(env.action_space.sample()) 54 env.step(env.action_space.sample()) 60 env.step(env.action_space.sample()) 61 env.step(env.action_space.sample()) 63 env.step(env.action_space.sample())
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H A D | batch_env.py | 45 action_space = self._envs[0].action_space 46 if not all(env.action_space == action_space for env in self._envs): 82 if not env.action_space.contains(action):
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H A D | wrappers.py | 90 action = self._env.action_space.sample() 200 self._is_finite(self._env.action_space)) 217 def action_space(self): member in RangeNormalize 218 space = self._env.action_space 238 min_ = self._env.action_space.low 239 max_ = self._env.action_space.high 263 def action_space(self): member in ClipAction 264 shape = self._env.action_space.shape 268 action_space = self._env.action_space 269 action = np.clip(action, action_space.low, action_space.high) [all …]
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H A D | mock_algorithm.py | 38 shape = (len(self._envs),) + self._envs[0].action_space.shape 39 low = self._envs[0].action_space.low 40 high = self._envs[0].action_space.high
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/dports/math/py-gym/gym-0.21.0/gym/vector/ |
H A D | sync_vector_env.py | 32 def __init__(self, env_fns, observation_space=None, action_space=None, copy=True): argument 38 if (observation_space is None) or (action_space is None): 40 action_space = action_space or self.envs[0].action_space 44 action_space=action_space,
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H A D | vector_env.py | 32 def __init__(self, num_envs, observation_space, action_space): argument 37 self.action_space = Tuple((action_space,) * num_envs) 45 self.single_action_space = action_space
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/dports/devel/py-bullet3/bullet3-3.21/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/ |
H A D | action_denormalize_wrapper.py | 10 low = np.array(env.action_space.low) 11 high = np.array(env.action_space.high) 22 self.action_space = gym.spaces.Box( 25 shape=self._gym_env.action_space.low.shape,
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/dports/devel/py-bullet3/bullet3-3.21/examples/pybullet/gym/pybullet_envs/agents/tools/ |
H A D | batch_env.py | 45 action_space = self._envs[0].action_space 46 if not all(env.action_space == action_space for env in self._envs): 81 if not env.action_space.contains(action):
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H A D | wrappers.py | 92 action = self._env.action_space.sample() 202 self._is_finite(self._env.action_space)) 219 def action_space(self): member in RangeNormalize 220 space = self._env.action_space 240 min_ = self._env.action_space.low 241 max_ = self._env.action_space.high 265 def action_space(self): member in ClipAction 266 shape = self._env.action_space.shape 270 action_space = self._env.action_space 271 action = np.clip(action, action_space.low, action_space.high) [all …]
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H A D | mock_algorithm.py | 40 shape = (tf.shape(agent_indices)[0],) + self._envs[0].action_space.shape 41 low = self._envs[0].action_space.low 42 high = self._envs[0].action_space.high
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/dports/devel/py-bullet3/bullet3-3.21/examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/ |
H A D | space_utils.py | 120 def create_constant_action(action_space, action_value=0): argument 122 if isinstance(action_space, gym.spaces.Dict): 126 for sub_name, sub_space in action_space.spaces.items() 129 return np.full(action_space.shape, action_value)
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/dports/math/py-gym/gym-0.21.0/gym/ |
H A D | core.py | 38 action_space = None variable in Env 249 def action_space(self): member in Wrapper 251 return self.env.action_space 254 @action_space.setter 255 def action_space(self, space): member in Wrapper
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/dports/devel/py-bullet3/bullet3-3.21/examples/pybullet/gym/pybullet_envs/examples/ |
H A D | enjoy_TF_HalfCheetahBulletEnv_v0_2017may.py | 20 def __init__(self, observation_space, action_space): argument 23 assert weights_final_w.shape == (64, action_space.shape[0]) 37 pi = SmallReactivePolicy(env.observation_space, env.action_space)
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H A D | enjoy_TF_HopperBulletEnv_v0_2017may.py | 22 def __init__(self, observation_space, action_space): argument 25 assert weights_final_w.shape == (64, action_space.shape[0]) 39 pi = SmallReactivePolicy(env.observation_space, env.action_space)
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H A D | enjoy_TF_Walker2DBulletEnv_v0_2017may.py | 20 def __init__(self, observation_space, action_space): argument 23 assert weights_final_w.shape == (64, action_space.shape[0]) 36 pi = SmallReactivePolicy(env.observation_space, env.action_space)
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H A D | enjoy_TF_InvertedPendulumBulletEnv_v0_2017may.py | 20 def __init__(self, observation_space, action_space): argument 23 assert weights_final_w.shape == (32, action_space.shape[0]) 37 pi = SmallReactivePolicy(env.observation_space, env.action_space)
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H A D | enjoy_TF_InvertedPendulumSwingupBulletEnv_v0_2017may.py | 20 def __init__(self, observation_space, action_space): argument 23 assert weights_final_w.shape == (32.0, action_space.shape[0]) 37 pi = SmallReactivePolicy(env.observation_space, env.action_space)
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H A D | enjoy_TF_AntBulletEnv_v0_2017may.py | 20 def __init__(self, observation_space, action_space): argument 23 assert weights_final_w.shape == (64, action_space.shape[0]) 38 pi = SmallReactivePolicy(env.observation_space, env.action_space)
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H A D | enjoy_TF_HumanoidBulletEnv_v0_2017may.py | 20 def __init__(self, observation_space, action_space): argument 23 assert weights_final_w.shape == (128, action_space.shape[0]) 37 pi = SmallReactivePolicy(env.observation_space, env.action_space)
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H A D | enjoy_TF_InvertedDoublePendulumBulletEnv_v0_2017may.py | 20 def __init__(self, observation_space, action_space): argument 23 assert weights_final_w.shape == (32.0, action_space.shape[0]) 37 pi = SmallReactivePolicy(env.observation_space, env.action_space)
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