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data/ppo_playground_loco_spotgetup_2026_03_14_213703/info/ppo_playground_loco_spotgetup_t0_s1_session_df_eval.csv

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data/ppo_playground_loco_spotgetup_2026_03_14_213703/info/ppo_playground_loco_spotgetup_t0_s1_session_metrics_scalar_eval.json

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+}

data/ppo_playground_loco_spotgetup_2026_03_14_213703/info/ppo_playground_loco_spotgetup_t0_s1_session_metrics_scalar_train.json

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data/ppo_playground_loco_spotgetup_2026_03_14_213703/info/ppo_playground_loco_spotgetup_t0_s2_session_metrics_scalar_eval.json

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+}

data/ppo_playground_loco_spotgetup_2026_03_14_213703/info/ppo_playground_loco_spotgetup_t0_s2_session_metrics_scalar_train.json

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data/ppo_playground_loco_spotgetup_2026_03_14_213703/info/ppo_playground_loco_spotgetup_t0_s3_session_metrics_scalar_eval.json

@@ -0,0 +1,9 @@
+{
+  "total_reward_ma": 23.0372371673584,
+  "strength": 23.197843551635742,
+  "max_strength": 47.73892593383789,
+  "final_strength": 29.251203536987305,
+  "sample_efficiency": 3.6508613021624114e-08,
+  "training_efficiency": 1.875462112366222e-05,
+  "stability": 0.9042580723762512
+}

data/ppo_playground_loco_spotgetup_2026_03_14_213703/info/ppo_playground_loco_spotgetup_t0_s3_session_metrics_scalar_train.json

@@ -0,0 +1,9 @@
+{
+  "total_reward_ma": 23.0372371673584,
+  "strength": 23.197843551635742,
+  "max_strength": 47.73892593383789,
+  "final_strength": 29.251203536987305,
+  "sample_efficiency": 3.6508613021624114e-08,
+  "training_efficiency": 1.875462112366222e-05,
+  "stability": 0.9042580723762512
+}

data/ppo_playground_loco_spotgetup_2026_03_14_213703/info/ppo_playground_loco_spotgetup_t0_trial_metrics_scalar.json

@@ -0,0 +1,11 @@
+{
+  "frame": 100001792.0,
+  "total_reward_ma": 19.385299682617188,
+  "strength": 21.747713088989258,
+  "max_strength": 47.799015045166016,
+  "final_strength": 17.75255012512207,
+  "sample_efficiency": 3.747531707176677e-08,
+  "training_efficiency": 1.924958087329287e-05,
+  "stability": 0.9108732342720032,
+  "consistency": 0.5110427141189575
+}

data/ppo_playground_loco_spotgetup_2026_03_14_213703/log/ppo_playground_loco_spotgetup_t0.log

@@ -0,0 +1,5 @@
+2026-03-14 22:21:30 | INFO | PID:1553 | slm_lab.lib.logger:info | trial_metrics: frame:1.00e+08 | total_reward_ma:19.39 | strength:21.75 | max_strength:47.8 | final_strength:17.75 | sample_efficiency:3.748e-08 | training_efficiency:1.925e-05 | stability:0.9109 | consistency:0.511
+2026-03-14 22:21:30 | INFO | PID:1553 | slm_lab.lib.logger:info | Trial 0 done
+2026-03-14 22:21:30 | INFO | PID:1553 | slm_lab.cli.main:run_spec | Output: data/ppo_playground_loco_spotgetup_2026_03_14_213703
+2026-03-14 22:21:30 | INFO | PID:1553 | slm_lab.lib.hf:cleanup_models | Cleanup saved 15.2 MB
+2026-03-14 22:21:31 | INFO | PID:1553 | slm_lab.lib.hf:upload | Uploading data/ppo_playground_loco_spotgetup_2026_03_14_213703 to SLM-Lab/benchmark-dev/data/ppo_playground_loco_spotgetup_2026_03_14_213703...

data/ppo_playground_loco_spotgetup_2026_03_14_213703/log/ppo_playground_loco_spotgetup_t0_s0.log

@@ -0,0 +1,140 @@
+2026-03-14 21:37:08 | INFO | PID:1671 | slm_lab.env:make_env | Playground device: GPU (cuda) — DLPack zero-copy
+2026-03-14 21:37:19 | INFO | PID:1671 | slm_lab.env:_make_playground_env | Playground: JAX→PyTorch via DLPack zero-copy (GPU)
+2026-03-14 21:37:23 | INFO | PID:1671 | slm_lab.lib.perf:log_perf_setup | Performance setup:
+Platform: GPU NVIDIA RTX A4500 (compute 8.6)
+• GPU cuDNN benchmark: enabled
+• GPU memory management: enabled
+• GPU TF32 acceleration: enabled
+2026-03-14 21:37:23 | INFO | PID:1671 | slm_lab.lib.logger:info | PPO:
+algorithm_spec:
+  name: PPO
+  action_pdtype: Normal
+  gamma: 0.97
+  lam: 0.95
+  clip_eps_spec:
+    name: no_decay
+    start_val: 0.3
+  entropy_coef_spec:
+    name: no_decay
+    start_val: 0.01
+  val_loss_coef: 0.5
+  minibatch_size: 4096
+  normalize_v_targets: false
+  time_horizon: 20
+  training_epoch: 4
+name: PPO
+memory_spec:
+  name: OnPolicyBatchReplay
+action_pdtype: Normal
+entropy_coef_spec:
+  name: no_decay
+  start_val: 0.01
+minibatch_size: 4096
+val_loss_coef: 0.5
+num_unrolls: 1
+normalize_v_targets: False
+clip_vloss: False
+symlog: False
+normalize_advantages: standardize
+gamma: 0.97
+lam: 0.95
+clip_eps_spec:
+  name: no_decay
+  start_val: 0.3
+time_horizon: 20
+training_epoch: 4
+to_train: 0
+_unroll_batches: []
+training_frequency: 20
+clip_eps: 0.3
+shared: False
+net: (device:cuda:0) TorchArcNet(
+  (body): GraphModule(
+    (body): Sequential(
+      (0): Linear(in_features=30, out_features=128, bias=True)
+      (1): SiLU()
+      (2): Linear(in_features=128, out_features=128, bias=True)
+      (3): SiLU()
+      (4): Linear(in_features=128, out_features=128, bias=True)
+      (5): SiLU()
+      (6): Linear(in_features=128, out_features=128, bias=True)
+      (7): SiLU()
+    )
+  )
+  (tails): ModuleList(
+    (0-1): 2 x Sequential(
+      (0): Linear(in_features=128, out_features=12, bias=True)
+    )
+  )
+  (loss_fn): MSELoss()
+)
+net_names: ['net', 'critic_net']
+critic_net: (device:cuda:0) TorchArcNet(
+  (body): GraphModule(
+    (body): Sequential(
+      (0): Linear(in_features=30, out_features=256, bias=True)
+      (1): SiLU()
+      (2): Linear(in_features=256, out_features=256, bias=True)
+      (3): SiLU()
+      (4): Linear(in_features=256, out_features=256, bias=True)
+      (5): SiLU()
+      (6): Linear(in_features=256, out_features=256, bias=True)
+      (7): SiLU()
+      (8): Linear(in_features=256, out_features=256, bias=True)
+      (9): SiLU()
+    )
+  )
+  (tails): Sequential(
+    (0): Linear(in_features=256, out_features=1, bias=True)
+  )
+  (loss_fn): MSELoss()
+)
+optim: Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    decoupled_weight_decay: False
+    differentiable: False
+    eps: 1e-05
+    foreach: None
+    fused: None
+    lr: 0.0003
+    maximize: False
+    weight_decay: 0
+)
+critic_optim: Adam (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    decoupled_weight_decay: False
+    differentiable: False
+    eps: 1e-05
+    foreach: None
+    fused: None
+    lr: 0.0003
+    maximize: False
+    weight_decay: 0
+)
+old_net: (device:cuda:0) TorchArcNet(
+  (body): GraphModule(
+    (body): Sequential(
+      (0): Linear(in_features=30, out_features=128, bias=True)
+      (1): SiLU()
+      (2): Linear(in_features=128, out_features=128, bias=True)
+      (3): SiLU()
+      (4): Linear(in_features=128, out_features=128, bias=True)
+      (5): SiLU()
+      (6): Linear(in_features=128, out_features=128, bias=True)
+      (7): SiLU()
+    )
+  )
+  (tails): ModuleList(
+    (0-1): 2 x Sequential(
+      (0): Linear(in_features=128, out_features=12, bias=True)
+    )
+  )
+  (loss_fn): MSELoss()
+)
+2026-03-14 21:37:23 | INFO | PID:1671 | slm_lab.lib.logger:info | Random baseline unavailable for playground/SpotGetup, defaulting to 0.