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data/ppo_atari_kungfumaster_2026_01_06_182954/info/ppo_atari_kungfumaster_t0_s0_session_df_train.csv

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data/ppo_atari_kungfumaster_2026_01_06_182954/info/ppo_atari_kungfumaster_t0_s1_session_metrics_scalar_eval.json

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data/ppo_atari_kungfumaster_2026_01_06_182954/info/ppo_atari_kungfumaster_t0_s1_session_metrics_scalar_train.json

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data/ppo_atari_kungfumaster_2026_01_06_182954/info/ppo_atari_kungfumaster_t0_s2_session_metrics_scalar_eval.json

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data/ppo_atari_kungfumaster_2026_01_06_182954/info/ppo_atari_kungfumaster_t0_s2_session_metrics_scalar_train.json

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data/ppo_atari_kungfumaster_2026_01_06_182954/info/ppo_atari_kungfumaster_t0_s3_session_metrics_scalar_eval.json

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data/ppo_atari_kungfumaster_2026_01_06_182954/info/ppo_atari_kungfumaster_t0_s3_session_metrics_scalar_train.json

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data/ppo_atari_kungfumaster_2026_01_06_182954/info/ppo_atari_kungfumaster_t0_trial_metrics_scalar.json

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data/ppo_atari_kungfumaster_2026_01_06_182954/log/ppo_atari_kungfumaster_t0.log

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+2026-01-06 19:47:54 | INFO | PID:1248 | slm_lab.lib.logger:info | trial_metrics: frame:1.00e+07 | total_reward_ma:18973.42 | strength:1.555e+04 | max_strength:19723 | final_strength:1.89e+04 | sample_efficiency:3.489e-07 | training_efficiency:2.232e-05 | stability:0.9971 | consistency:0.5454
+2026-01-06 19:47:54 | INFO | PID:1248 | slm_lab.lib.logger:info | Trial 0 done
+2026-01-06 19:47:54 | INFO | PID:1248 | slm_lab.cli.main:run_spec | Output: data/ppo_atari_kungfumaster_2026_01_06_182954
+2026-01-06 19:47:54 | INFO | PID:1248 | slm_lab.lib.hf:cleanup_models | Cleanup saved 103.4 MB
+2026-01-06 19:47:54 | INFO | PID:1248 | slm_lab.lib.hf:upload | Uploading data/ppo_atari_kungfumaster_2026_01_06_182954 to SLM-Lab/benchmark-dev/data/ppo_atari_kungfumaster_2026_01_06_182954...

data/ppo_atari_kungfumaster_2026_01_06_182954/log/ppo_atari_kungfumaster_t0_s0.log

@@ -0,0 +1,986 @@
+2026-01-06 18:29:56 | INFO | PID:1330 | slm_lab.env:_make_vector_env | AtariVectorEnv: 16 envs, native preprocessing
+2026-01-06 18:30:35 | INFO | PID:1330 | slm_lab.lib.perf:log_perf_setup | Performance setup:
+Platform: GPU NVIDIA GeForce RTX 3080 (compute 8.6)
+• GPU cuDNN benchmark: enabled
+• GPU memory management: enabled
+• GPU TF32 acceleration: enabled
+2026-01-06 18:30:35 | INFO | PID:1330 | slm_lab.lib.logger:info | PPO:
+algorithm_spec:
+  name: PPO
+  action_pdtype: default
+  action_policy: default
+  explore_var_spec: null
+  gamma: 0.99
+  lam: 0.95
+  clip_eps_spec:
+    name: no_decay
+    start_val: 0.1
+    end_val: 0.1
+    start_step: 0
+    end_step: 0
+  entropy_coef_spec:
+    name: no_decay
+    start_val: 0.01
+    end_val: 0.01
+    start_step: 0
+    end_step: 0
+  val_loss_coef: 0.5
+  time_horizon: 128
+  minibatch_size: 256
+  training_epoch: 4
+  clip_vloss: true
+name: PPO
+memory_spec:
+  name: OnPolicyBatchReplay
+action_pdtype: default
+entropy_coef_spec:
+  name: no_decay
+  start_val: 0.01
+  end_val: 0.01
+  start_step: 0
+  end_step: 0
+minibatch_size: 256
+val_loss_coef: 0.5
+normalize_v_targets: False
+symlog_transform: False
+clip_vloss: True
+gamma: 0.99
+lam: 0.95
+clip_eps_spec:
+  name: no_decay
+  start_val: 0.1
+  end_val: 0.1
+  start_step: 0
+  end_step: 0
+time_horizon: 128
+training_epoch: 4
+to_train: 0
+training_frequency: 128
+clip_eps: 0.1
+shared: True
+net: (device:cuda:0) ConvNet(
+  (conv_model): Sequential(
+    (0): Conv2d(4, 32, kernel_size=(8, 8), stride=(4, 4))
+    (1): ReLU()
+    (2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2))
+    (3): ReLU()
+    (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
+    (5): ReLU()
+  )
+  (fc_model): Sequential(
+    (0): Linear(in_features=3136, out_features=512, bias=True)
+    (1): ReLU()
+  )
+  (tails): ModuleList(
+    (0): Sequential(
+      (0): Linear(in_features=512, out_features=14, bias=True)
+    )
+    (1): Sequential(
+      (0): Linear(in_features=512, out_features=1, bias=True)
+    )
+  )
+  (loss_fn): MSELoss()
+)
+net_names: ['net']
+optim: AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: (0.9, 0.999)
+    capturable: False
+    decoupled_weight_decay: True
+    differentiable: False
+    eps: 1e-05
+    foreach: None
+    fused: None
+    initial_lr: 0.00025
+    lr: 0.00025
+    maximize: False
+    weight_decay: 0.01
+)
+old_net: (device:cuda:0) ConvNet(
+  (conv_model): Sequential(
+    (0): Conv2d(4, 32, kernel_size=(8, 8), stride=(4, 4))
+    (1): ReLU()
+    (2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2))
+    (3): ReLU()
+    (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
+    (5): ReLU()
+  )
+  (fc_model): Sequential(
+    (0): Linear(in_features=3136, out_features=512, bias=True)
+    (1): ReLU()
+  )
+  (tails): ModuleList(
+    (0): Sequential(
+      (0): Linear(in_features=512, out_features=14, bias=True)
+    )
+    (1): Sequential(
+      (0): Linear(in_features=512, out_features=1, bias=True)
+    )
+  )
+  (loss_fn): MSELoss()
+)
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+frame:1.00e+04          wall_t:43               t:625                   epi:nan
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+2026-01-06 18:30:47 | INFO | PID:1330 | slm_lab.agent:log_summary | Trial 0 session 0 ppo_atari_kungfumaster_t0_s0 [train]
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