Fix README markdownlint errors

README.md

@@ -1,4 +1,4 @@
----
+---
 title: Cascade Containment
 emoji: 🦠
 colorFrom: red
@@ -8,7 +8,7 @@ app_port: 7860
 pinned: false
 ---
 
-# 🦠 Cascade Containment
+## 🦠 Cascade Containment
 
 > An RL benchmark for sequential resource allocation under spreading cascade dynamics.
 
@@ -34,19 +34,22 @@ No existing OpenEnv benchmark formalizes this problem class. Cascade Containment
 ## Environment Design
 
 ### Action Space
+
 One decision per step — kept deliberately simple to maximize strategic depth:
 
-| Field | Type | Values |
-|-------|------|--------|
-| `action_type` | string | `"test"` · `"restrict"` · `"allocate"` |
-| `district_id` | int | 0-indexed district target |
+| Field        | Type   | Values                              |
+|--------------|--------|-------------------------------------|
+| `action_type`| string | `"test"` · `"restrict"` · `"allocate"` |
+| `district_id`| int    | 0-indexed district target           |
 
 - **test** — spend 1 resource to get accurate infection data for a district
 - **restrict** — impose movement restriction (free, but penalised if infection is low)
 - **allocate** — deploy 1 resource unit to reduce spread rate this step
 
 ### Observation Space
+
 The agent receives a filtered, potentially lagged view of the world — never the full ground truth:
+
 ```python
 CityObservation:
   districts: List[DistrictObservation]  # per-district visible state
@@ -59,23 +62,25 @@ CityObservation:
 ```
 
 Each `DistrictObservation` contains:
+
 - `reported_infection_rate` — real-time (easy/medium) or **3 days lagged** (hard)
 - `growth_rate_hint` — noisy signal of true spread rate
 - `hospital_capacity_remaining` — always accurate (hospitals report in real time)
 - `tested_recently`, `restriction_active`
 
-### The Key Design Decision: Partial Observability
+### Partial Observability
+
 The hard task exposes infection rates from **3 days ago**. The agent must learn to act on noisy forward signals (`growth_rate_hint`) rather than react to confirmed data — exactly the challenge real public health officials face. This single mechanic is what separates a thoughtful agent from a reactive one.
 
 ---
 
 ## Three Tasks
 
-| Task | Districts | Steps | Resources | Data Lag | Challenge |
-|------|-----------|-------|-----------|----------|-----------|
-| `easy` | 2 | 10 | 10 | None | Single outbreak, clear signal |
-| `medium` | 4 | 15 | 8 | None | Two simultaneous outbreaks, forced triage |
-| `hard` | 6 | 20 | 7 | 3 days | Scarce resources, invisible acceleration |
+| Task     | Districts | Steps | Resources | Data Lag | Challenge                                |
+|----------|-----------|-------|-----------|----------|------------------------------------------|
+| `easy`   | 2         | 10    | 10        | None     | Single outbreak, clear signal            |
+| `medium` | 4         | 15    | 8         | None     | Two simultaneous outbreaks, forced triage|
+| `hard`   | 6         | 20    | 7         | 3 days   | Scarce resources, invisible acceleration |
 
 ---
 
@@ -83,15 +88,15 @@ The hard task exposes infection rates from **3 days ago**. The agent must learn
 
 Five shaped reward terms fire independently each step, providing dense feedback throughout the episode:
 
-| Term | Value | Purpose |
-|------|-------|---------|
-| Infection penalty | `-0.50` per district above threshold | Primary containment signal |
-| Hospital breach | `-1.00` per collapsed hospital | Catastrophic failure deterrent |
-| Early containment | `+0.50 × (1 - step/max_steps)` | Teaches proactive behaviour |
-| Unnecessary restriction | `-0.20` | Prevents lazy blanket lockdowns |
-| Correct prioritisation | `+0.30` | Rewards triage intelligence |
+| Term                    | Value                          | Purpose                          |
+|-------------------------|--------------------------------|----------------------------------|
+| Infection penalty       | `-0.50` per district above threshold | Primary containment signal  |
+| Hospital breach         | `-1.00` per collapsed hospital | Catastrophic failure deterrent   |
+| Early containment       | `+0.50 × (1 - step/max_steps)` | Teaches proactive behaviour      |
+| Unnecessary restriction | `-0.20`                        | Prevents lazy blanket lockdowns  |
+| Correct prioritisation  | `+0.30`                        | Rewards triage intelligence      |
 
-The early containment bonus decays over time — containing an outbreak on day 3 is worth more than on day 8. This single design decision is what teaches the agent to act before crises emerge rather than after.
+The early containment bonus decays over time — containing an outbreak on day 3 is worth more than on day 8. This design decision teaches the agent to act before crises emerge rather than after.
 
 ---
 
@@ -99,12 +104,12 @@ The early containment bonus decays over time — containing an outbreak on day 3
 
 The grader is fully deterministic — no randomness, no LLM calls — producing a weighted composite score in `[0.0, 1.0]`:
 
-| Component | Weight | Measures |
-|-----------|--------|---------|
-| Containment score | 45% | District-days below infection threshold |
-| Hospital score | 30% | Capacity preserved across episode |
-| Efficiency score | 15% | Resources directed to high-need districts |
-| Speed score | 10% | Containment achieved faster than max steps |
+| Component         | Weight | Measures                                  |
+|-------------------|--------|-------------------------------------------|
+| Containment score | 45%    | District-days below infection threshold   |
+| Hospital score    | 30%    | Capacity preserved across episode         |
+| Efficiency score  | 15%    | Resources directed to high-need districts |
+| Speed score       | 10%    | Containment achieved faster than max steps|
 
 ---
 
@@ -126,17 +131,16 @@ The prompt is the policy. Memory updates are the policy improvement. This produc
 
 Dumb greedy policy (always allocates to district 0):
 
-| Task | Score | Hospital Breached |
-|------|-------|-------------------|
-| Easy | ~0.50 | No |
-| Medium | ~0.23 | Yes |
-| Hard | ~0.21 | Yes |
-
-A smart LLM agent using the episodic memory baseline consistently scores 0.65–0.80 on easy and shows meaningful improvement on medium across rollouts.
+| Task   | Score | Hospital Breached |
+|--------|-------|-------------------|
+| Easy   | ~0.50 | No                |
+| Medium | ~0.23 | Yes               |
+| Hard   | ~0.21 | Yes               |
 
 ---
 
 ## Usage
+
 ```python
 from client import CascadeContainmentEnv
 from models import ContainmentAction
@@ -144,28 +148,22 @@ from models import ContainmentAction
 with CascadeContainmentEnv(
     base_url="https://therubberduckdebuggers-cascade-containment.hf.space"
 ).sync() as env:
-    # Run easy task
     obs = env.reset(task_name="easy")
-    
+
     while not obs.done:
-        action = ContainmentAction(
-            action_type="allocate",
-            district_id=0
-        )
-        result = env.step(action)
+        result = env.step(ContainmentAction(action_type="allocate", district_id=0))
         obs = result.observation
         print(f"Reward: {result.reward:.4f}")
 ```
 
 ### Running the Full Evaluation
+
 ```bash
-# Set required environment variables
 export API_BASE_URL="https://router.huggingface.co/v1"
 export MODEL_NAME="meta-llama/Llama-3.1-8B-Instruct"
 export HF_TOKEN="your_hf_token"
 export ENV_BASE_URL="https://therubberduckdebuggers-cascade-containment.hf.space"
 
-# Run inference
 python inference.py
 ```
 
@@ -173,19 +171,20 @@ python inference.py
 
 ## Generalisation
 
-This environment is not epidemic-specific. The core mechanics — spreading cascade, delayed data, resource scarcity, spatial spillover — are identical to:
+This environment is not epidemic-specific. The core mechanics — spreading cascade, delayed data, resource scarcity, spatial spillover — apply to:
 
 - **Wildfire deployment** — pre-position crews before fire reaches populated areas
 - **Cyberattack isolation** — quarantine systems before lateral movement completes
 - **Misinformation containment** — deploy corrections before false narratives entrench
-- **Poverty intervention** — allocate aid where need is growing, not just where it's visible
+- **Poverty intervention** — allocate aid where need is growing, not just where it is visible
 
 The environment is designed to be a lasting benchmark for this general problem class, not a pandemic novelty.
 
 ---
 
 ## Project Structure
-```
+
+```text
 epidemic_containment_env/
 ├── models.py              # Data contracts (Action, Observation, State)
 ├── constants.py           # All numeric configuration
@@ -212,4 +211,5 @@ epidemic_containment_env/
 ---
 
 ## Tags
-`reinforcement-learning` · `resource-allocation` · `sequential-decision-making` · `partial-observability` · `cascade-dynamics` · `openenv` · `llm-agent`
+
+`reinforcement-learning` · `resource-allocation` · `sequential-decision-making` · `partial-observability` · `cascade-dynamics` · `openenv` · `llm-agent`