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Add Atlantis and Asterix zero-latency H8 CPU small policies

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zero-latency/asterix/small-policy/sample-factory-hist8-cpu15m-s0-v1/README.md ADDED
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+ # Asterix Sample Factory H8 zero-latency policy
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+
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+ This policy was trained from scratch with CPU/ALE simulation, four stacked image frames, and the previous eight decision actions as input.
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+
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+ - Training: 15 million environment steps, seed 0, 60/15 environment/observation FPS
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+ - Evaluation: deterministic 100 episodes, case IDs 42–141, CPU/ALE, zero latency, 14,400-frame horizon, 32 parallel environments
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+ - Selected checkpoint: `best_000024112_12345344_reward_5614.500.pth`
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+ - Mean return: 6041.000; standard deviation: 2272.101
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+ - Student-t 95% CI: [5590.166, 6491.834]
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+
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+ The earlier `sample-factory-v1` teacher used a 5,000-frame evaluation horizon; its mean return is not directly comparable to this result. The checkpoint filename's training reward is not an independent evaluation score.
zero-latency/asterix/small-policy/sample-factory-hist8-cpu15m-s0-v1/best_000024112_12345344_reward_5614.500.pth ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:0fe54ff4b3a2ccec438aca789f1a9f86fcef7cda44cda5156e3b774262c50beb
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+ size 18563174
zero-latency/asterix/small-policy/sample-factory-hist8-cpu15m-s0-v1/config.json ADDED
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+ {
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+ "help": false,
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+ "algo": "APPO",
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+ "env": "latency_gymnasium",
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+ "experiment": "asterix_sf_zero_latency_hist8_cpu15m_seed0",
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+ "train_dir": "results/checkpoints/atari_zero_hist8_cpu15m",
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+ "restart_behavior": "resume",
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+ "device": "gpu",
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+ "seed": 0,
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+ "initial_model_path": null,
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+ "num_policies": 1,
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+ "async_rl": true,
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+ "serial_mode": false,
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+ "batched_sampling": false,
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+ "num_batches_to_accumulate": 2,
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+ "worker_num_splits": 1,
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+ "policy_workers_per_policy": 1,
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+ "max_policy_lag": 300,
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+ "num_workers": 4,
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+ "num_envs_per_worker": 16,
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+ "batch_size": 1024,
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+ "num_batches_per_epoch": 8,
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+ "num_epochs": 2,
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+ "rollout": 64,
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+ "fasttd3_replay_capacity": 6553600,
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+ "fasttd3_replay_batch_size": 32768,
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+ "fasttd3_action_chunk_horizon": 1,
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+ "fasttd3_transitions_per_update": 64,
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+ "fasttd3_train_for_optimizer_steps": 10000000000,
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+ "fasttd3_v_min": -250.0,
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+ "fasttd3_v_max": 250.0,
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+ "fasttd3_actor_action_l2": 0.0,
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+ "fasttd3_compile": true,
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+ "recurrence": 32,
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+ "shuffle_minibatches": false,
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+ "gamma": 0.99,
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+ "reward_scale": 1.0,
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+ "reward_clip": 1000.0,
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+ "value_bootstrap": false,
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+ "normalize_returns": true,
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+ "exploration_loss_coeff": 0.003,
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+ "value_loss_coeff": 0.5,
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+ "kl_loss_coeff": 0.0,
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+ "exploration_loss": "entropy",
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+ "gae_lambda": 0.95,
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+ "ppo_clip_ratio": 0.1,
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+ "ppo_clip_value": 0.2,
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+ "with_vtrace": false,
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+ "vtrace_rho": 1.0,
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+ "vtrace_c": 1.0,
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+ "optimizer": "adam",
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+ "adam_eps": 1e-06,
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+ "adam_beta1": 0.9,
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+ "adam_beta2": 0.999,
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+ "max_grad_norm": 0.0,
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+ "learning_rate": 0.000303,
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+ "lr_schedule": "constant",
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+ "lr_schedule_kl_threshold": 0.008,
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+ "lr_adaptive_min": 1e-06,
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+ "lr_adaptive_max": 0.01,
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+ "obs_subtract_mean": 0.0,
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+ "obs_scale": 1.0,
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+ "normalize_input": true,
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+ "normalize_input_keys": null,
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+ "decorrelate_experience_max_seconds": 0,
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+ "decorrelate_envs_on_one_worker": true,
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+ "actor_worker_gpus": [],
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+ "set_workers_cpu_affinity": true,
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+ "force_envs_single_thread": false,
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+ "default_niceness": 0,
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+ "log_to_file": true,
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+ "experiment_summaries_interval": 1,
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+ "flush_summaries_interval": 30,
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+ "stats_avg": 100,
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+ "summaries_use_frameskip": true,
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+ "heartbeat_interval": 20,
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+ "heartbeat_reporting_interval": 180,
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+ "train_for_env_steps": 15000000,
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+ "train_for_seconds": 10000000000,
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+ "save_every_sec": 600,
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+ "keep_checkpoints": 5,
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+ "load_checkpoint_kind": "latest",
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+ "save_milestones_sec": -1,
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+ "save_best_every_sec": 5,
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+ "save_best_metric": "reward",
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+ "save_best_after": 100000,
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+ "benchmark": false,
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+ "encoder_mlp_layers": [
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+ 512,
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+ 512
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+ ],
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+ "encoder_conv_architecture": "convnet_simple",
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+ "encoder_conv_mlp_layers": [
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+ 512
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+ ],
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+ "use_rnn": false,
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+ "rnn_size": 512,
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+ "rnn_type": "gru",
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+ "rnn_num_layers": 1,
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+ "decoder_mlp_layers": [],
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+ "nonlinearity": "elu",
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+ "policy_initialization": "orthogonal",
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+ "policy_init_gain": 1.0,
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+ "actor_critic_share_weights": true,
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+ "adaptive_stddev": false,
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+ "continuous_tanh_scale": 0.0,
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+ "initial_stddev": 1.0,
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+ "use_env_info_cache": false,
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+ "env_gpu_actions": false,
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+ "env_gpu_observations": true,
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+ "env_frameskip": 1,
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+ "env_framestack": 1,
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+ "pixel_format": "CHW",
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+ "use_record_episode_statistics": false,
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+ "episode_counter": false,
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+ "with_wandb": true,
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+ "wandb_user": null,
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+ "wandb_project": "latency-sensitive-bench",
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+ "wandb_group": "asterix",
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+ "wandb_job_type": "sample_factory",
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+ "wandb_tags": null,
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+ "wandb_dir": "/mnt/ssd/codex/atari-zero-hist8-20260924/wandb",
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+ "with_pbt": false,
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+ "pbt_mix_policies_in_one_env": true,
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+ "pbt_period_env_steps": 5000000,
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+ "pbt_start_mutation": 20000000,
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+ "pbt_replace_fraction": 0.3,
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+ "pbt_mutation_rate": 0.15,
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+ "pbt_replace_reward_gap": 0.1,
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+ "pbt_replace_reward_gap_absolute": 1e-06,
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+ "pbt_optimize_gamma": false,
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+ "pbt_target_objective": "true_objective",
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+ "pbt_perturb_min": 1.1,
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+ "pbt_perturb_max": 1.5,
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+ "gym_task_name": "asterix",
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+ "gym_env_id": "LatencyBench/Asterix-v0",
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+ "gym_make_kwargs_json": "{\"base_env_id\": \"ALE/Asterix-v5\", \"render_mode\": \"rgb_array\", \"screen_size\": 84, \"noop_max\": 30, \"base_make_kwargs\": {\"obs_type\": \"rgb\", \"frameskip\": 1, \"repeat_action_probability\": 0.0, \"full_action_space\": false, \"mode\": 0, \"difficulty\": 0, \"max_num_frames_per_episode\": 108000}}",
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+ "gym_registration_imports_json": "[\"latency_bench.envs.gymnasium_asterix\"]",
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+ "gym_action_space_json": "{\"type\": \"discrete\", \"labels\": [\"noop\", \"up\", \"right\", \"left\", \"down\", \"upright\", \"upleft\", \"downright\", \"downleft\"], \"values\": [0, 1, 2, 3, 4, 5, 6, 7, 8]}",
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+ "gym_action_labels_json": "[\"noop\", \"up\", \"right\", \"left\", \"down\", \"upright\", \"upleft\", \"downright\", \"downleft\"]",
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+ "gym_action_values_json": "[0, 1, 2, 3, 4, 5, 6, 7, 8]",
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+ "gym_noop_action_id": 0,
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+ "gym_noop_action_json": "\"noop\"",
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+ "gym_base_prompt": "Collect useful objects and avoid lyres. Choose exactly one action from: noop, up, right, left, down, upright, upleft, downright, downleft.",
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+ "gym_state_labels_json": "",
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+ "env_fps": 60.0,
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+ "obs_fps": 15.0,
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+ "frame_stack": 4,
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+ "action_history_decisions": 8,
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+ "simulator": "cpu",
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+ "gpu_env_device": "auto",
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+ "gpu_env_batch_size": 1,
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+ "noop_max": null,
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+ "terminal_on_life_loss": null,
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+ "clip_reward": null,
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+ "max_episode_steps": null,
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+ "export_env_raw_rgb_frames": false,
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+ "mode": "train",
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+ "latency_type": "zero",
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+ "fixed_latency_ms": null,
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+ "mean_latency_ms": null,
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+ "std_latency_ms": null,
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+ "min_latency_ms": null,
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+ "max_latency_ms": null,
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+ "latency_seed": null,
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+ "latency_profile_path": null,
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+ "latency_profile_worker_slot": null,
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+ "simulated_worker_capacity": null,
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+ "add_latency_info": false,
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+ "max_pending_actions": null,
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+ "hold_last_chunk_action": false,
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+ "hold_policy": "hold",
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+ "ordering_policy": "issue_order_fifo",
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+ "eval_episodes": 5,
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+ "eval_parallel_envs": 1,
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+ "eval_latency_raw_frame_values": null,
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+ "eval_max_steps": 5000,
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+ "eval_deterministic": true,
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+ "eval_raw_reward": false,
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+ "episode_metrics_path": "results/checkpoints/atari_zero_hist8_cpu15m/asterix_sf_zero_latency_hist8_cpu15m_seed0/episode_metrics.jsonl",
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+ "ppo": null,
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+ "command_line": "--mode train --algo APPO --env latency_gymnasium --experiment asterix_sf_zero_latency_hist8_cpu15m_seed0 --train_dir results/checkpoints/atari_zero_hist8_cpu15m --restart_behavior resume --device gpu --seed 0 --episode_metrics_path results/checkpoints/atari_zero_hist8_cpu15m/asterix_sf_zero_latency_hist8_cpu15m_seed0/episode_metrics.jsonl --train_for_env_steps 15000000 --num_workers 4 --num_envs_per_worker 16 --num_policies 1 --batch_size 1024 --rollout 64 --recurrence 32 --num_epochs 2 --num_batches_per_epoch 8 --worker_num_splits 1 --max_policy_lag 300 --learning_rate 0.000303 --lr_schedule constant --nonlinearity elu --gamma 0.99 --gae_lambda 0.95 --ppo_clip_ratio 0.1 --ppo_clip_value 0.2 --exploration_loss entropy --exploration_loss_coeff 0.003 --value_loss_coeff 0.5 --max_grad_norm 0.0 --adam_eps 1e-06 --obs_scale 1.0 --save_every_sec 600 --keep_checkpoints 5 --stats_avg 100 --experiment_summaries_interval 1 --async_rl True --use_rnn False --normalize_returns True --normalize_input True --adaptive_stddev False --with_vtrace False --latency-type zero --add-latency-info False --eval-episodes 5 --eval-parallel-envs 1 --eval-max-steps 5000 --eval-deterministic True --with_wandb True --wandb_project latency-sensitive-bench --wandb_group asterix --wandb_job_type sample_factory --encoder_conv_architecture convnet_simple --gym-task-name asterix --gym-env-id LatencyBench/Asterix-v0 --gym-make-kwargs-json {\"base_env_id\": \"ALE/Asterix-v5\", \"render_mode\": \"rgb_array\", \"screen_size\": 84, \"noop_max\": 30, \"base_make_kwargs\": {\"obs_type\": \"rgb\", \"frameskip\": 1, \"repeat_action_probability\": 0.0, \"full_action_space\": false, \"mode\": 0, \"difficulty\": 0, \"max_num_frames_per_episode\": 108000}} --gym-registration-imports-json [\"latency_bench.envs.gymnasium_asterix\"] --gym-action-space-json {\"type\": \"discrete\", \"labels\": [\"noop\", \"up\", \"right\", \"left\", \"down\", \"upright\", \"upleft\", \"downright\", \"downleft\"], \"values\": [0, 1, 2, 3, 4, 5, 6, 7, 8]} --gym-noop-action-json \"noop\" --gym-action-labels-json [\"noop\", \"up\", \"right\", \"left\", \"down\", \"upright\", \"upleft\", \"downright\", \"downleft\"] --gym-action-values-json [0, 1, 2, 3, 4, 5, 6, 7, 8] --gym-noop-action-id 0 --gym-base-prompt Collect useful objects and avoid lyres. Choose exactly one action from: noop, up, right, left, down, upright, upleft, downright, downleft. --env-fps 60 --obs-fps 15 --frame-stack 4 --action-history-decisions 8 --simulator cpu --hold-policy hold --ordering-policy issue_order_fifo --use_env_info_cache False",
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+ "cli_args": {
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+ "algo": "APPO",
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+ "env": "latency_gymnasium",
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+ "experiment": "asterix_sf_zero_latency_hist8_cpu15m_seed0",
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+ "train_dir": "results/checkpoints/atari_zero_hist8_cpu15m",
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+ "restart_behavior": "resume",
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+ "device": "gpu",
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+ "seed": 0,
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+ "num_policies": 1,
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+ "async_rl": true,
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+ "worker_num_splits": 1,
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+ "max_policy_lag": 300,
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+ "num_workers": 4,
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+ "num_envs_per_worker": 16,
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+ "batch_size": 1024,
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+ "num_batches_per_epoch": 8,
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+ "num_epochs": 2,
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+ "rollout": 64,
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+ "recurrence": 32,
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+ "gamma": 0.99,
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+ "normalize_returns": true,
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+ "exploration_loss_coeff": 0.003,
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+ "value_loss_coeff": 0.5,
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+ "exploration_loss": "entropy",
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+ "gae_lambda": 0.95,
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+ "ppo_clip_ratio": 0.1,
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+ "ppo_clip_value": 0.2,
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+ "with_vtrace": false,
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+ "adam_eps": 1e-06,
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+ "max_grad_norm": 0.0,
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+ "learning_rate": 0.000303,
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+ "lr_schedule": "constant",
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+ "obs_scale": 1.0,
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+ "normalize_input": true,
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+ "experiment_summaries_interval": 1,
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+ "stats_avg": 100,
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+ "train_for_env_steps": 15000000,
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+ "save_every_sec": 600,
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+ "keep_checkpoints": 5,
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+ "encoder_conv_architecture": "convnet_simple",
223
+ "use_rnn": false,
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+ "nonlinearity": "elu",
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+ "adaptive_stddev": false,
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+ "use_env_info_cache": false,
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+ "with_wandb": true,
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+ "wandb_project": "latency-sensitive-bench",
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+ "wandb_group": "asterix",
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+ "wandb_job_type": "sample_factory",
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+ "gym_task_name": "asterix",
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+ "gym_env_id": "LatencyBench/Asterix-v0",
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+ "gym_make_kwargs_json": "{\"base_env_id\": \"ALE/Asterix-v5\", \"render_mode\": \"rgb_array\", \"screen_size\": 84, \"noop_max\": 30, \"base_make_kwargs\": {\"obs_type\": \"rgb\", \"frameskip\": 1, \"repeat_action_probability\": 0.0, \"full_action_space\": false, \"mode\": 0, \"difficulty\": 0, \"max_num_frames_per_episode\": 108000}}",
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+ "gym_registration_imports_json": "[\"latency_bench.envs.gymnasium_asterix\"]",
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+ "gym_action_space_json": "{\"type\": \"discrete\", \"labels\": [\"noop\", \"up\", \"right\", \"left\", \"down\", \"upright\", \"upleft\", \"downright\", \"downleft\"], \"values\": [0, 1, 2, 3, 4, 5, 6, 7, 8]}",
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+ "gym_action_labels_json": "[\"noop\", \"up\", \"right\", \"left\", \"down\", \"upright\", \"upleft\", \"downright\", \"downleft\"]",
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+ "gym_action_values_json": "[0, 1, 2, 3, 4, 5, 6, 7, 8]",
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+ "gym_noop_action_id": 0,
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+ "gym_noop_action_json": "\"noop\"",
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+ "gym_base_prompt": "Collect useful objects and avoid lyres. Choose exactly one action from: noop, up, right, left, down, upright, upleft, downright, downleft.",
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+ "env_fps": 60.0,
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+ "obs_fps": 15.0,
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+ "frame_stack": 4,
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+ "action_history_decisions": 8,
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+ "simulator": "cpu",
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+ "mode": "train",
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+ "latency_type": "zero",
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+ "add_latency_info": false,
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+ "hold_policy": "hold",
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+ "ordering_policy": "issue_order_fifo",
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+ "eval_episodes": 5,
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+ "eval_parallel_envs": 1,
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+ "eval_max_steps": 5000,
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+ "eval_deterministic": true,
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+ "episode_metrics_path": "results/checkpoints/atari_zero_hist8_cpu15m/asterix_sf_zero_latency_hist8_cpu15m_seed0/episode_metrics.jsonl",
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+ "output_dir": "outputs/atari_zero_hist8_cpu15m/asterix/train"
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+ },
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+ "git_hash": "unknown",
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+ "git_repo_name": "not a git repository",
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+ "eval_env_frameskip": 1,
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+ "output_dir": "outputs/atari_zero_hist8_cpu15m/asterix/train",
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+ "wandb_unique_id": "asterix_sf_zero_latency_hist8_cpu15m_seed0"
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+ # Atlantis Sample Factory H8 zero-latency policy
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+
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+ This policy was trained from scratch with CPU/ALE simulation, four stacked image frames, and the previous eight decision actions as input.
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+
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+ - Training: 15 million environment steps, seed 0, 60/15 environment/observation FPS
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+ - Evaluation: deterministic 100 episodes, case IDs 42–141, CPU/ALE, zero latency, 14,400-frame horizon, 32 parallel environments
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+ - Selected checkpoint: `best_000026560_13598720_reward_675769.000.pth`
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+ - Mean return: 88613.000; standard deviation: 22690.414
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+ - Student-t 95% CI: [84110.730, 93115.270]
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+
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+ The earlier `sample-factory-v1` teacher used a 5,000-frame evaluation horizon; its mean return is not directly comparable to this result. The checkpoint filename's training reward is not an independent evaluation score.
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