fix some typos
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@ -500,7 +500,7 @@ for _v in _versions:
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kwargs_dict_box_pushing_prodmp['phase_generator_kwargs']['alpha_phase'] = 3
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kwargs_dict_box_pushing_prodmp['phase_generator_kwargs']['alpha_phase'] = 3
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kwargs_dict_box_pushing_prodmp['black_box_kwargs']['max_planning_times'] = 2
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kwargs_dict_box_pushing_prodmp['black_box_kwargs']['max_planning_times'] = 2
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kwargs_dict_box_pushing_prodmp['black_box_kwargs']['replanning_schedule'] = lambda pos, vel, obs, action, t : t % 25 == 0
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kwargs_dict_box_pushing_prodmp['black_box_kwargs']['replanning_schedule'] = lambda pos, vel, obs, action, t : t % 25 == 0
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kwargs_dict_box_pushing_prodmp['black_box_kwargs']['condition_on_desried'] = True
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kwargs_dict_box_pushing_prodmp['black_box_kwargs']['condition_on_desired'] = True
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register(
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register(
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id=_env_id,
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id=_env_id,
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entry_point='fancy_gym.utils.make_env_helpers:make_bb_env_helper',
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entry_point='fancy_gym.utils.make_env_helpers:make_bb_env_helper',
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@ -33,7 +33,7 @@ def example_custom_replanning_envs(seed=0, iteration=100, render=True):
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# condition_on_desired: use desired state as the boundary condition for the next plan
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# condition_on_desired: use desired state as the boundary condition for the next plan
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black_box_kwargs = {'max_planning_times': 4,
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black_box_kwargs = {'max_planning_times': 4,
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'replanning_schedule': lambda pos, vel, obs, action, t: t % 25 == 0,
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'replanning_schedule': lambda pos, vel, obs, action, t: t % 25 == 0,
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'desired_traj_bc': True}
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'condition_on_desired': True}
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env = fancy_gym.make_bb(env_id=base_env_id, wrappers=wrappers, black_box_kwargs=black_box_kwargs,
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env = fancy_gym.make_bb(env_id=base_env_id, wrappers=wrappers, black_box_kwargs=black_box_kwargs,
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traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs,
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traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs,
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@ -59,4 +59,4 @@ if __name__ == "__main__":
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example_run_replanning_env(env_name="BoxPushingDenseReplanProDMP-v0", seed=1, iterations=1, render=False)
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example_run_replanning_env(env_name="BoxPushingDenseReplanProDMP-v0", seed=1, iterations=1, render=False)
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# run a custom replanning environment
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# run a custom replanning environment
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example_custom_replanning_envs(seed=0, iteration=100, render=True)
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example_custom_replanning_envs(seed=0, iteration=8, render=True)
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