temporal updates
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@@ -40,6 +40,8 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
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# Now the action space is not the raw action but the parametrization of the trajectory generator,
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# such as a ProMP
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ac = env.action_space.sample()
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# ac[0] = 0.6866657733917236
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# ac[1] = 0.08587364107370377
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# This executes a full trajectory and gives back the context (obs) of the last step in the trajectory, or the
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# full observation space of the last step, if replanning/sub-trajectory learning is used. The 'reward' is equal
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# to the return of a trajectory. Default is the sum over the step-wise rewards.
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@@ -50,7 +52,8 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
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if done:
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# print(reward)
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obs = env.reset()
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print("steps: {}".format(info["num_steps"][-1]))
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print("=================New Episode======================")
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# print("steps: {}".format(info["num_steps"][-1]))
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def example_custom_mp(env_name="Reacher5dProMP-v0", seed=1, iterations=1, render=True):
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@@ -158,17 +161,18 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
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if __name__ == '__main__':
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render = True
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render = False
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# DMP
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# example_mp("HoleReacherDMP-v0", seed=10, iterations=5, render=render)
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# ProMP
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# example_mp("HoleReacherProMP-v0", seed=10, iterations=5, render=render)
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# example_mp("BoxPushingTemporalSparseProMP-v0", seed=10, iterations=1, render=render)
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example_mp("TableTennis4DProMP-v0", seed=10, iterations=10, render=True)
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# example_mp("TableTennis4DProMP-v0", seed=10, iterations=10, render=True)
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# ProDMP
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# example_mp("BoxPushingDenseProDMP-v0", seed=10, iterations=16, render=render)
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example_mp("TableTennis4DProDMP-v0", seed=10, iterations=5000, render=render)
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# Altered basis functions
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# obs1 = example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=1, render=render)
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