Update to post-retrain hp-consistent 5-condition checkpoints; minimal README

README.md

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----
-license: cc-by-4.0
----
+# Spatial-memory checkpoints (5 visual conditions)
 
-# Spatial-memory checkpoints
+Frozen post-training DD-PPO PointNav agents on Habitat for five visual sensor
+conditions on a shared ResNet-18 + 3-layer LSTM (512-d) backbone. Hidden
+state `h_2` (top LSTM layer) is the canonical 512-d cognitive-map readout.
+
+| folder              | encoder                                                | final ckpt |
+| ------------------- | ------------------------------------------------------ | ---------- |
+| `blind/`            | no visual encoder                                      | `ckpt.34.pth` |
+| `coarse/`           | 48 x 48 RGB, 1 x 1 encoder feature map                 | `ckpt.49.pth` |
+| `foveated/`         | 256 x 256 RGB, eccentricity Gaussian blur, 4 x 4 map   | `ckpt.49.pth` |
+| `foveated_logpolar/`| 64 x 64 log-polar resampled, ~2 x 2 map                | `ckpt.49.pth` |
+| `uniform/`          | 256 x 256 RGB, no blur, 4 x 4 map                      | `ckpt.49.pth` |
+
+Intermediate ckpts (training-trajectory snapshots) are also included:
+sighted at `ckpt.{10,20,30,40,49}.pth`; blind at
+`ckpt.{5,10,15,20,25,30,34}.pth`.
+
+## Load a checkpoint
 
 ```python
 import torch
-ckpt = torch.load("blind/ckpt.34.pth", map_location="cpu", weights_only=False)
-state_dict = ckpt["state_dict"]
+from huggingface_hub import hf_hub_download
+
+cond = "foveated"          # or: blind | coarse | uniform | foveated_logpolar
+ckpt_path = hf_hub_download(
+    repo_id="alunxu/spatial-memory-checkpoints",
+    filename=f"{cond}/ckpt.49.pth",
+)
+
+ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)
+state_dict = ckpt["state_dict"]              # actor-critic policy weights
+config     = ckpt["config"]                  # habitat-baselines OmegaConf
+```
+
+Each `.pth` is a habitat-baselines checkpoint with keys `state_dict`,
+`config`, and `extra_state` (training step counters).
+
+## Rebuild the policy and read `h_2`
+
+```python
+from habitat_baselines.common.baseline_registry import baseline_registry
+from habitat_baselines.utils.common import get_action_space_info
+from habitat_baselines.config.default import get_config
+from habitat.config.default_structured_configs import HabitatConfigPlugin
+
+# 1. Build the same env the policy was trained on (for obs/action spaces).
+env_config = config.habitat                  # already inside ckpt
+# ... construct the eval env from env_config (see code repo) ...
+
+# 2. Instantiate the policy class registered for this config and load weights.
+policy_cls = baseline_registry.get_policy(
+    config.habitat_baselines.rl.policy.name)
+policy = policy_cls.from_config(
+    config=config,
+    observation_space=env.observation_space,
+    action_space=env.action_space,
+)
+policy.load_state_dict(state_dict)
+policy.eval()
+
+# 3. Run a rollout. The recurrent hidden state has shape
+#    (num_envs, num_layers=3, hidden=512). h_2 is the top layer:
+#       h_2 = recurrent_hidden_states[:, 2, :]
+#    Pass `recurrent_hidden_states` back into `policy.act(...)` each step.
 ```
+
+Code, configs, and the deterministic-rollout probing pipeline that produced
+this release: <https://github.com/alunxu/foveated-cog-map>.

foveated/ckpt.36.pth → blind/ckpt.15.pth

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