reacher adjustments
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@@ -39,14 +39,18 @@ class ALRReacherEnv(MujocoEnv, utils.EzPickle):
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reward_dist = 0.0
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angular_vel = 0.0
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reward_balance = 0.0
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is_delayed = self.steps_before_reward > 0
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reward_ctrl = - np.square(a).sum()
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if self._steps >= self.steps_before_reward:
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vec = self.get_body_com("fingertip") - self.get_body_com("target")
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reward_dist -= self.reward_weight * np.linalg.norm(vec)
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if self.steps_before_reward > 0:
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if is_delayed:
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# avoid giving this penalty for normal step based case
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# angular_vel -= 10 * np.linalg.norm(self.sim.data.qvel.flat[:self.n_links])
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angular_vel -= 10 * np.square(self.sim.data.qvel.flat[:self.n_links]).sum()
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reward_ctrl = - 10 * np.square(a).sum()
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if is_delayed:
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# Higher control penalty for sparse reward per timestep
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reward_ctrl *= 10
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if self.balance:
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reward_balance -= self.balance_weight * np.abs(
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