change parameter
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@@ -1,5 +1,4 @@
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import random
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import numpy
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import numpy as np
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import torch
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@@ -343,7 +342,7 @@ class T1GetUpRewardCfg:
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# 7. 成功终极大奖
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is_success = RewTerm(
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func=lambda env, keys: env.termination_manager.get_term(keys),
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weight=1000.0,
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weight=300.0,
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params={"keys": "standing_success"}
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)
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@@ -383,4 +382,4 @@ class T1EnvCfg(ManagerBasedRLEnvCfg):
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actions = T1ActionCfg()
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episode_length_s = 6.0
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decimation = 4
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decimation = 2
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