exp05b_ppo_4x4_from_bc: checkpoint, config, evaluation files, model card

README.md

@@ -42,7 +42,7 @@ model-index:
 
 # 4d-snake-exp05b-ppo-4x4-from-bc
 
-A `512x512` MLP policy for **4-dimensional snake on the 4^4 board** (256 cells, 8 moves): MaskablePPO fine-tuned from the behaviour-cloned network, no curriculum, 20,000,000 environment steps. 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-rl/blob/main/docs/evaluation.md) (100 episodes x 3 seeds, masked `evaluate_policy`).
+A `512x512` MLP policy for **4-dimensional snake on the 4^4 board** (256 cells, 8 moves): MaskablePPO fine-tuned from the behaviour-cloned network, no curriculum, 20,000,000 environment steps. 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`)
 
@@ -53,10 +53,10 @@ A `512x512` MLP policy for **4-dimensional snake on the 4^4 board** (256 cells,
 
 ## How to use
 
-The observation is this repository's `4*C + 2` float vector and the action space its `2*ndim` masked moves ([docs/game_rules.md](https://github.com/BurnyCoder/4d-snake-rl/blob/main/docs/game_rules.md)), so the checkpoint runs inside `snake4d`'s environment:
+The observation is this repository's `4*C + 2` float vector and the action space its `2*ndim` masked moves ([docs/game_rules.md](https://github.com/BurnyCoder/4d-snake-reinforcement-learning-agent/blob/main/docs/game_rules.md)), so the checkpoint runs inside `snake4d`'s environment:
 
 ```bash
-git clone https://github.com/BurnyCoder/4d-snake-rl.git && cd 4d-snake-rl && uv sync
+git clone https://github.com/BurnyCoder/4d-snake-reinforcement-learning-agent.git && cd 4d-snake-reinforcement-learning-agent && uv sync
 hf download BurnyCoder/4d-snake-exp05b-ppo-4x4-from-bc best_model.zip --local-dir weights
 uv run snake4d evaluate --set model_path=weights/best_model.zip --set size=4 --set ndim=4
 ```
@@ -80,7 +80,7 @@ Use deterministic mode: the cloned policy follows a fixed Hamiltonian cycle and
 
 ## Training
 
-- Phase `train`; experiment file `experiments/exp05b_ppo_4x4_from_bc.env`; write-up: https://github.com/BurnyCoder/4d-snake-rl/blob/main/reports/experiments/exp05_bc_4x4.md.
+- Phase `train`; experiment file `experiments/exp05b_ppo_4x4_from_bc.env`; write-up: https://github.com/BurnyCoder/4d-snake-reinforcement-learning-agent/blob/main/reports/experiments/exp05_bc_4x4.md.
 - Resolved configuration (`config.json`):
 
 ```json
@@ -138,9 +138,9 @@ Use deterministic mode: the cloned policy follows a fixed Hamiltonian cycle and
 
 ## Provenance
 
-- Code: https://github.com/BurnyCoder/4d-snake-rl at commit `016b5dc97583b7c086ad172226997e6aeea92dca`.
+- Code: https://github.com/BurnyCoder/4d-snake-reinforcement-learning-agent at commit `016b5dc97583b7c086ad172226997e6aeea92dca`.
 - Library versions (`versions.json`): 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, cuda_device NVIDIA GeForce RTX 5070 Laptop GPU.
-- `eval/summary.json` and `eval/eval_episodes.csv` are the files the repository's reports quote; every evaluated network is compared in [reports/networks.md](https://github.com/BurnyCoder/4d-snake-rl/blob/main/reports/networks.md).
+- `eval/summary.json` and `eval/eval_episodes.csv` are the files the repository's reports quote; every evaluated network is compared in [reports/networks.md](https://github.com/BurnyCoder/4d-snake-reinforcement-learning-agent/blob/main/reports/networks.md).
 - Collection: https://huggingface.co/collections/BurnyCoder/4d-snake-rl-all-evaluated-networks-6a9d0a0a66c7efcd101b7741
 
 ## Files