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��# RL-Based Court Scheduling - Hackathon-Ready Plan
Simplified & Explainable RL Framework for Judicial Scheduling
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## Problem Formulation
### Why RL for Court Scheduling?
Court scheduling is a sequential resource allocation problem under constraints:
- Sequential: Today's listing affects future readiness and delays
- Stochastic: Hearings may progress or adjourn unpredictably
- Multi-objective: Fairness, efficiency, backlog reduction, urgency handling
- Dynamic environment: New cases arrive, some stagnate, some progress
Why simplified RL approach?
- RL learns "which cases benefit most from being listed now"
- RL adapts to different scheduling scenarios (backlog-heavy, urgent-heavy)
- RL provides priority score while fairness/constraints remain rule-based
- This hybrid keeps system transparent and avoids historical bias
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## MDP Formulation
### State Space: Per Case, Not Global
Each case has a fixed 6-dimensional state vector:
python case_state = { "stage": stage_encoded, # 0-7 stage "age_days": normalized_age, # 0-1 scaling "days_since_last": normalized_delay, # 0-1 "urgency": 0 or 1, "ripe": 0 or 1, "hearing_count": normalized_count, # 0-1 }
Why per-case states?
- Avoids huge global state space
- Keeps RL simple: one decision per case per day
- Easy to explain and validate
### Action Space: Binary Decision Per Case
For each case:
python action = 1 # schedule today action = 0 # skip today
Final scheduling per courtroom is done by separate, deterministic allocator that:
- Respects daily limits
- Ensures urgent cases are always listed
- Guarantees fairness (no long-term starvation)
### Reward Function: Minimal & Explainable
python reward = ( +2 if case progresses + -1 if adjourned + +3 if urgent & scheduled + -2 if unripe & scheduled + +1 if long pending & scheduled )
Why simple?
- RL will converge faster
- Rewards map directly to judicial objectives
- Easy to justify to judges
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## System Architecture (Hybrid: Rules + RL)
This hybrid system aligns with judicial constraints and fairness:
������������������������������������������������������������������������������ ��� RULE-BASED FILTERING ��� ��� (fairness, ripeness) ��� ������������������������������������������������������������������������������ ��� cases pass �����������������������������������]%������������������������������������������ ��� RL PRIORITY MODEL ��� ��� (one case at a time) ��� ������������������������������������������������������������������������������ ��� Q-score �����������������������������������]%������������������������������������������ ��� ALLOCATION ENGINE ��� ��� (courtroom limits) ��� ������������������������������������������������������������������������������ ��� cause list �����������������������������������]%������������������������������������������ ��� 2-YEAR SIMULATION ��� ������������������������������������������������������������������������������
What RL controls: Only priority score of each case
What RL does NOT control: fairness, daily load, urgent overrides, courtroom capacity, ripeness rules
## Implementation Phases (Hackathon-Friendly)
### Phase 1 - Environment Setup (Day 1)
- Build minimal OpenAI Gym-like environment
- Encode case states
- Implement binary action step()
- Create transition logic based on hearing patterns
### Phase 2 - RL Model (Day 1-2)
Use Tabular Q-learning or Linear Q-learning:
- Very fast to train
- Transparent & interpretable
- No neural networks required
- Avoids state dimensionality explosion
Update Rule: Q(s,a) ��� Q(s,a) + l%�%(r + l%% max Q(s',a') - Q(s,a))
### Phase 3 - Daily Scheduler (Day 2)
1. Compute Q-value for each case
2. Sort cases by Q-score
3. Apply fairness constraints (urgent first, max waiting time, stage balancing)
4. Allocate cases to 5 courtrooms respecting daily limits
### Phase 4 - Evaluation (Day 2-3)
Compare baseline vs RL+rules for:
- disposal rate
- adjournment rate
- fairness (waiting-time variance)
- % urgent cases scheduled same week
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## Technical Stack Simplified
Dependencies:
numpy pandas or polars python-gym-like wrapper (custom, minimal)
No deep learning frameworks needed. Training time: minutes
### Project Structure
scheduler/ ��������� core/ # Existing (unchanged) ��������� simulation/ # Existing (unchanged) ��������� rl/ # New RL components (minimal) ��������� __init__.py ��������� simple_agent.py # Tabular Q-learning ��������� training.py # Training loop ��������� explainability.py # Decision explanations
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## Interpretability & Bias Mitigation (Critical for Judges)
### Techniques:
1. Feature importance plot: RL Q-value contribution for each dimension
2. Counterfactual checks: "If urgency flag was removed, does scheduling change?"
3. Fairness constraints enforced in allocation: RL cannot override fairness rules
4. Reward engineering avoids historical bias: Reward = progress, not past scheduling patterns
## Expected Outcomes
### Realistic Improvements
- Better case prioritization: Learn which cases benefit most from being listed
- Adaptive to scenarios: Backlog-heavy vs urgent-heavy patterns
- Explainable decisions: Can show why each case prioritized
- Fast training: Minutes, not hours
### Success Criteria
- Performance: RL disposal rate >= heuristic disposal rate
- Speed: Training completes in <30 minutes
- Explainability: Every decision has clear reasoning
- Control: Judges can override any RL decision
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## Implementation Timeline
### Day 1: Environment Setup
- Build minimal case environment
- Implement tabular Q-learning agent
- Test with random baseline
### Day 2: Integration & Training
- Integrate RL with existing ReadinessPolicy
- Train agent on 50 episodes (10 minutes)
- Validate convergence
### Day 3: Evaluation & Polish
- Compare RL vs heuristic performance
- Add explainability functions
- Create simple demo
### Backup Plan
If RL training fails:
- Keep existing heuristic as default
- RL becomes optional feature (--use-rl flag)
- Still demonstrates RL thinking in documentation
## Final Deliverables
1. Working RL scheduler prototype
2. 2-year simulated cause lists (CSV)
3. Fairness & bias mitigation strategy
4. Explainable decision system
5. 3-minute demo video
This simplified RL plan captures the spirit of RL while being fully explainable, responsible, implementable in 48-72 hours, and aligned with hackathon demands on fairness, clarity & real-world viability.
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Last Updated: 2025-11-25
Status: Hackathon-Ready - Simplified Approach
Algorithm: Tabular Q-Learning for Priority Scoring
Timeline: 3 Days Implementation