hackathon_code4change / RL_EXPLORATION_PLAN.md
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Add simplified RL exploration plan for hackathon
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��# RL-Based Court Scheduling - Hackathon-Ready Plan Simplified & Explainable RL Framework for Judicial Scheduling --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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. --- Last Updated: 2025-11-25 Status: Hackathon-Ready - Simplified Approach Algorithm: Tabular Q-Learning for Priority Scoring Timeline: 3 Days Implementation