Make ORI_IND optional and adjust the size of maze for swimmer
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409ee44568
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d043a35e6c
@ -11,7 +11,7 @@ from gym.utils import EzPickle
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class AgentModel(ABC, MujocoEnv, EzPickle):
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class AgentModel(ABC, MujocoEnv, EzPickle):
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FILE: str
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FILE: str
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MANUAL_COLLISION: bool
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MANUAL_COLLISION: bool
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ORI_IND: int
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ORI_IND: Optional[int] = None
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RADIUS: Optional[float] = None
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RADIUS: Optional[float] = None
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def __init__(self, file_path: str, frame_skip: int) -> None:
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def __init__(self, file_path: str, frame_skip: int) -> None:
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@ -331,7 +331,7 @@ class SubGoalTRoom(GoalRewardTRoom):
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class NoRewardRoom(MazeTask):
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class NoRewardRoom(MazeTask):
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REWARD_THRESHOLD: float = 0.0
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REWARD_THRESHOLD: float = 0.0
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MAZE_SIZE_SCALING: Scaling = Scaling(4.0, 4.0, 4.0)
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MAZE_SIZE_SCALING: Scaling = Scaling(4.0, 4.0, 1.0)
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def reward(self, obs: np.ndarray) -> float:
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def reward(self, obs: np.ndarray) -> float:
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return 0.0
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return 0.0
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@ -37,7 +37,6 @@ class SwimmerEnv(AgentModel):
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def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, dict]:
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def step(self, action: np.ndarray) -> Tuple[np.ndarray, float, bool, dict]:
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xy_pos_before = self.sim.data.qpos[:2].copy()
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xy_pos_before = self.sim.data.qpos[:2].copy()
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self.do_simulation(action, self.frame_skip)
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self.do_simulation(action, self.frame_skip)
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forward_reward = self._forward_reward(xy_pos_before)
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forward_reward = self._forward_reward(xy_pos_before)
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ctrl_cost = self._ctrl_cost_weight * np.sum(np.square(action))
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ctrl_cost = self._ctrl_cost_weight * np.sum(np.square(action))
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return (
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return (
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