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v1.0.2-rc1; mv rem_cts to moe_cts, moe_cts to moe_no_goal_cts, fix rem params same as moe, add --robogauge to start

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wty-yy
2026-01-26 17:48:34 +08:00
parent 2aed91e7ce
commit 1798e67c29
23 changed files with 438 additions and 409 deletions
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24
legged_gym/envs/base/legged_robot_config.py
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@@ -302,6 +302,10 @@ class LeggedRobotCfgPPO(BaseConfig):
checkpoint = -1 # -1 = last saved model
resume_path = None # updated from load_run and chkpt
class robogauge:
enabled = False
port = 9973
class LeggedRobotCfgCTS(BaseConfig):
seed = 0
runner_class_name = "OnPolicyRunnerCTS"
@@ -350,7 +354,11 @@ class LeggedRobotCfgCTS(BaseConfig):
checkpoint = -1 # -1 = last saved model
resume_path = None # updated from load_run and chkpt
class LeggedRobotCfgMoECTS(LeggedRobotCfgCTS):
class robogauge:
enabled = False
port = 9973
class LeggedRobotCfgMoENGCTS(LeggedRobotCfgCTS):
class policy(LeggedRobotCfgCTS.policy):
obs_no_goal_mask = None # mask for observation without goal inputs
student_expert_num = 8 # number of experts in the student model
@@ -359,8 +367,8 @@ class LeggedRobotCfgMoECTS(LeggedRobotCfgCTS):
load_balance_coef = 0.01 # coefficient for load balance loss
class runner(LeggedRobotCfgCTS.runner):
policy_class_name = 'ActorCriticMoECTS'
algorithm_class_name = 'MoECTS'
policy_class_name = 'ActorCriticMoENGCTS'
algorithm_class_name = 'MoENGCTS'
class LeggedRobotCfgMCPCTS(LeggedRobotCfgCTS):
class policy(LeggedRobotCfgCTS.policy):
@@ -382,20 +390,20 @@ class LeggedRobotCfgACMoECTS(LeggedRobotCfgCTS):
class LeggedRobotCfgDualMoECTS(LeggedRobotCfgCTS):
class policy(LeggedRobotCfgCTS.policy):
expert_num = 8 # number of experts in the student model
student_encoder_hidden_dims = [512, 256, 128]
student_encoder_hidden_dims = [512, 256, 256]
class runner(LeggedRobotCfgCTS.runner):
policy_class_name = 'ActorCriticDualMoECTS'
algorithm_class_name = 'DualMoECTS'
class LeggedRobotCfgREMCTS(LeggedRobotCfgCTS):
class LeggedRobotCfgMoECTS(LeggedRobotCfgCTS):
class policy(LeggedRobotCfgCTS.policy):
expert_num = 8 # number of experts in the student model
student_encoder_hidden_dims = [512, 256, 128]
student_encoder_hidden_dims = [512, 256, 256]
class algorithm(LeggedRobotCfgCTS.algorithm):
load_balance_coef = 0.01 # coefficient for load balance loss
class runner(LeggedRobotCfgCTS.runner):
policy_class_name = 'ActorCriticREMCTS'
algorithm_class_name = 'REMCTS'
policy_class_name = 'ActorCriticMoECTS'
algorithm_class_name = 'MoECTS'