exp05_bc_4x4: checkpoint, config, evaluation files, model card

README.md

@@ -43,7 +43,7 @@ model-index:
 
 # 4d-snake-exp05-bc-4x4
 
-A `512x512` MLP policy for **4-dimensional snake on the 4^4 board** (256 cells, 8 moves): behaviour cloning of the Hamiltonian route follower (`snake4d imitate`). From a length-1 start it completes the board in every deterministic evaluation episode, evaluated with the protocol of [docs/evaluation.md](https://github.com/BurnyCoder/4d-snake-reinforcement-learning-agent/blob/main/docs/evaluation.md) (100 episodes x 3 seeds, masked `evaluate_policy`).
+An MLP policy with two hidden layers of 512 units for **4-dimensional snake on the 4^4 board** (256 cells, 8 moves): behaviour cloning of the Hamiltonian route follower (`snake4d imitate`): 262,144 expert samples, 20 epochs, Adam 0.001. From a length-1 start it completes the board in every deterministic evaluation episode, evaluated with the protocol of [docs/evaluation.md](https://github.com/BurnyCoder/4d-snake-reinforcement-learning-agent/blob/main/docs/evaluation.md) (100 episodes x 3 seeds, masked `evaluate_policy`).
 
 ## Results (`eval/summary.json`)
 
@@ -77,7 +77,7 @@ masks = get_action_masks(env)  # the legal moves, one row per board
 action, _ = model.predict(obs, action_masks=masks, deterministic=True)
 ```
 
-Use deterministic mode: the cloned policy follows a fixed Hamiltonian cycle and sampled off-cycle moves eventually trap the snake (see the results table).
+Use deterministic mode: the cloned policy follows a fixed Hamiltonian cycle and sampled off-cycle moves eventually trap the snake (see the results table; analysis: https://github.com/BurnyCoder/4d-snake-reinforcement-learning-agent/blob/main/reports/experiments/exp05_bc_4x4.md).
 
 ## Training
 

config.json

@@ -1,46 +1,46 @@
-{
-  "size": 4,
-  "ndim": 4,
-  "idle_mult": 4,
-  "r_food": 1.0,
-  "r_death": -1.0,
-  "r_win": 10.0,
-  "r_step": -0.001,
-  "shaping_coef": 0.0,
-  "n_envs": 4096,
-  "total_timesteps": 20000000,
-  "n_steps": 64,
-  "batch_size": 8192,
-  "n_epochs": 4,
-  "gamma": 0.99,
-  "gae_lambda": 0.95,
-  "lr_start": 0.0003,
-  "lr_end": 1e-05,
-  "clip_start": 0.2,
-  "clip_end": 0.05,
-  "ent_coef": 0.01,
-  "vf_coef": 0.5,
-  "max_grad_norm": 0.5,
-  "target_kl": 0.03,
-  "net_width": 512,
-  "device": "auto",
-  "torch_threads": 8,
-  "seed": 0,
-  "curriculum": 1,
-  "curriculum_window": 4,
-  "curriculum_delta": 0,
-  "curriculum_rho": 0.9,
-  "curriculum_min_eps": 500,
-  "p_true_start": 0.2,
-  "eval_episodes": 100,
-  "eval_every": 2097152,
-  "ckpt_every": 8388608,
-  "eval_seeds": "0,1,2",
-  "bench_steps": 200000,
-  "bc_epochs": 20,
-  "bc_lr": 0.001,
-  "runs_dir": "runs",
-  "run_name": "exp05_bc_4x4",
-  "model_path": "",
-  "policy": "route"
+{
+  "size": 4,
+  "ndim": 4,
+  "idle_mult": 4,
+  "r_food": 1.0,
+  "r_death": -1.0,
+  "r_win": 10.0,
+  "r_step": -0.001,
+  "shaping_coef": 0.0,
+  "n_envs": 4096,
+  "total_timesteps": 20000000,
+  "n_steps": 64,
+  "batch_size": 8192,
+  "n_epochs": 4,
+  "gamma": 0.99,
+  "gae_lambda": 0.95,
+  "lr_start": 0.0003,
+  "lr_end": 1e-05,
+  "clip_start": 0.2,
+  "clip_end": 0.05,
+  "ent_coef": 0.01,
+  "vf_coef": 0.5,
+  "max_grad_norm": 0.5,
+  "target_kl": 0.03,
+  "net_width": 512,
+  "device": "auto",
+  "torch_threads": 8,
+  "seed": 0,
+  "curriculum": 1,
+  "curriculum_window": 4,
+  "curriculum_delta": 0,
+  "curriculum_rho": 0.9,
+  "curriculum_min_eps": 500,
+  "p_true_start": 0.2,
+  "eval_episodes": 100,
+  "eval_every": 2097152,
+  "ckpt_every": 8388608,
+  "eval_seeds": "0,1,2",
+  "bench_steps": 200000,
+  "bc_epochs": 20,
+  "bc_lr": 0.001,
+  "runs_dir": "runs",
+  "run_name": "exp05_bc_4x4",
+  "model_path": "",
+  "policy": "route"
 }

eval/eval_episodes.csv

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+    }
+  }
 }

versions.json

@@ -1,10 +1,10 @@
-{
-  "torch": "2.14.0+cu130",
-  "gymnasium": "1.3.0",
-  "stable-baselines3": "2.9.0",
-  "sb3-contrib": "2.9.0",
-  "numpy": "2.5.2",
-  "pygame-ce": "2.5.8",
-  "git_commit": "e483fe513be150f7ec5439b68d694e727b6b61dd",
-  "cuda_device": "NVIDIA GeForce RTX 5070 Laptop GPU"
+{
+  "torch": "2.14.0+cu130",
+  "gymnasium": "1.3.0",
+  "stable-baselines3": "2.9.0",
+  "sb3-contrib": "2.9.0",
+  "numpy": "2.5.2",
+  "pygame-ce": "2.5.8",
+  "git_commit": "e483fe513be150f7ec5439b68d694e727b6b61dd",
+  "cuda_device": "NVIDIA GeForce RTX 5070 Laptop GPU"
 }