docs: frame as offline-RL datasets (usable by any offline-RL method); add offline-reinforcement-learning tag

README.md

@@ -3,6 +3,7 @@ license: cc-by-4.0
 viewer: false
 tags:
   - reinforcement-learning
+  - offline-reinforcement-learning
   - unity-ml-agents
   - minari
   - causal-gpt-rl
@@ -11,8 +12,10 @@ tags:
 # Causal GPT-RL — Unity ML-Agents trajectories
 
 Recorded Unity ML-Agents trajectories packaged as
-[Minari](https://minari.farama.org) datasets, spanning continuous and discrete
-action spaces for testing Causal GPT-RL across space types. Every environment
+[Minari](https://minari.farama.org) datasets — **offline-RL datasets** spanning
+continuous and discrete action spaces. Any offline-RL method can train on them; we
+built them to develop **Causal GPT-RL**, our new approach that works across both
+space types. Every environment
 ships an `expert` tier; eight ship a full **quality ladder** (`expert` +
 calibrated `medium`/`simple`) synthesized by degrading the stock policy — Gaussian
 action-noise ranges for the continuous scenes, softmax-temperature ranges on the