change parameter
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@@ -41,19 +41,19 @@ params:
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normalize_advantage: True
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normalize_advantage: True
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gamma: 0.99
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gamma: 0.99
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tau: 0.95
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tau: 0.95
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learning_rate: 3e-4
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learning_rate: 2e-4
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lr_schedule: adaptive
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lr_schedule: adaptive
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kl_threshold: 0.008
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kl_threshold: 0.008
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score_to_win: 20000
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score_to_win: 20000
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max_epochs: 500000
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max_epochs: 500000
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save_best_after: 50
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save_best_after: 50
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save_frequency: 100
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save_frequency: 100
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grad_norm: 1.0
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grad_norm: 0.5
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entropy_coef: 0.05
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entropy_coef: 0.005
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truncate_grads: True
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truncate_grads: True
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bounds_loss_coef: 0.001
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bounds_loss_coef: 0.001
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e_clip: 0.2
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e_clip: 0.2
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horizon_length: 64
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horizon_length: 32
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minibatch_size: 4096
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minibatch_size: 4096
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mini_epochs: 5
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mini_epochs: 5
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critic_coef: 1
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critic_coef: 1
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@@ -1,5 +1,4 @@
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import random
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import random
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import numpy
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import numpy
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import numpy as np
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import numpy as np
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import torch
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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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# 7. 成功终极大奖
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is_success = RewTerm(
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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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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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params={"keys": "standing_success"}
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)
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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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actions = T1ActionCfg()
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episode_length_s = 6.0
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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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