| version: "1.0" |
| name: "TrafficSignalOptimization-v1" |
| description: > |
| AI-driven Traffic Signal Optimization for a 4-way urban intersection. |
| A reinforcement-learning environment that challenges agents to minimise |
| congestion, reduce average waiting time, respond to emergency vehicles, |
| and maintain signal stability across three difficulty tiers. |
| |
| author: "OpenEnv Submission" |
| tags: |
| - Reinforcement Learning |
| - Traffic Control |
| - Smart Cities |
| - Safety-Critical |
| - Emergency Vehicle Priority |
| licence: MIT |
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| environment: |
| class: "env.TrafficEnv" |
| entry_point: "env:TrafficEnv" |
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| state_space: |
| type: Dict |
| keys: |
| north_cars: |
| type: Discrete |
| description: "Queued vehicles in the North lane" |
| range: [0, max_queue] |
| south_cars: |
| type: Discrete |
| description: "Queued vehicles in the South lane" |
| range: [0, max_queue] |
| east_cars: |
| type: Discrete |
| description: "Queued vehicles in the East lane" |
| range: [0, max_queue] |
| west_cars: |
| type: Discrete |
| description: "Queued vehicles in the West lane" |
| range: [0, max_queue] |
| waiting_times: |
| type: "Dict[str, float]" |
| description: "Cumulative waiting-time pressure per lane (north/south/east/west)" |
| phase: |
| type: Discrete |
| values: [0, 1] |
| description: "Current green signal: 0 = NS green, 1 = EW green" |
| emergency_flags: |
| type: "Dict[str, bool]" |
| description: "True if an emergency vehicle is present in that lane" |
| step_count: |
| type: Discrete |
| description: "Current step within the episode" |
| range: [0, max_steps] |
|
|
| action_space: |
| type: Discrete |
| n: 2 |
| actions: |
| 0: "Keep current signal phase" |
| 1: "Switch signal phase (NS ↔ EW)" |
|
|
| observation_vector_dim: 14 |
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| |
| tasks: |
| - id: easy |
| description: "Stable, balanced traffic. Minimal emergencies. Ideal for learning." |
| config_key: easy |
| max_steps: 50 |
| score_range: [0.001, 0.999] |
| params: |
| arrival_rate: [0, 1] |
| discharge_rate: [4, 5] |
| max_queue: 15 |
| emergency_prob: 0.01 |
| burst_prob: 0.0 |
|
|
| - id: medium |
| description: "Random traffic bursts, moderate congestion, occasional emergencies." |
| config_key: medium |
| max_steps: 100 |
| score_range: [0.001, 0.999] |
| params: |
| arrival_rate: [1, 3] |
| discharge_rate: [3, 5] |
| max_queue: 25 |
| emergency_prob: 0.05 |
| burst_prob: 0.10 |
|
|
| - id: hard |
| description: "High-intensity traffic, frequent emergencies, strict fairness constraints." |
| config_key: hard |
| max_steps: 200 |
| score_range: [0.001, 0.999] |
| params: |
| arrival_rate: [2, 5] |
| discharge_rate: [2, 4] |
| max_queue: 40 |
| emergency_prob: 0.15 |
| burst_prob: 0.20 |
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| reward: |
| range: [-0.999, 0.999] |
| score_normalisation: "(reward + 1) / 2, clamped to [0.001, 0.999]" |
| components: |
| efficiency: |
| sign: "+" |
| description: "Vehicles cleared this step (throughput reward)" |
| congestion: |
| sign: "-" |
| description: "Normalised total queue density" |
| max_queue_penalty: |
| sign: "-" |
| description: "Penalty for extreme bottlenecks in any single lane" |
| switch_penalty: |
| sign: "-" |
| description: "Stability constraint to prevent oscillatory signal toggling" |
| improvement_bonus: |
| sign: "+" |
| description: "Bonus for active decongestion progress" |
| fairness_bonus: |
| sign: "+" |
| description: "Reward for maintaining balanced waiting times across all lanes" |
| starvation_penalty: |
| sign: "-" |
| description: "Penalty for phase-duration exceeding starvation limit" |
| emergency_golden_window: |
| sign: "+" |
| description: "Full bonus for clearing EV within golden window steps" |
| emergency_delay: |
| sign: "-" |
| description: "Exponential penalty for delaying life-saving vehicles" |
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| |
| metrics: |
| total_cleared: |
| type: int |
| description: "Total vehicles discharged from the intersection (episode)" |
| avg_waiting_time: |
| type: float |
| description: "Cumulative wait pressure divided by vehicles cleared" |
| max_queue_length: |
| type: int |
| description: "Peak queue length observed in any lane (episode)" |
| signal_switch_count: |
| type: int |
| description: "Total signal changes (lower = more stable)" |
| congestion_score: |
| type: float |
| range: [0.001, 0.999] |
| description: "Current normalised total queue depth" |
| avg_ev_clear_time: |
| type: float |
| description: "Average steps taken to clear an emergency vehicle" |
| fairness_score: |
| type: float |
| range: [0.001, 0.999] |
| description: "Index representing lane-level service balance" |
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| |
| baseline: |
| class: "baseline_agent.RuleBasedAgent" |
| description: > |
| Deterministic rule-based agent. Switches based on queue imbalance, |
| minimum green time, starvation guard, and emergency preemption. |
| parameters: |
| min_green_time: 5 |
| imbalance_threshold: 5 |
| max_green_time: 15 |
| emergency_min_green: 2 |
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| api: |
| reset: {method: POST, path: /reset, description: "Start a new episode"} |
| step: {method: POST, path: /step, description: "Advance one step"} |
| state: {method: GET, path: /state, description: "Get current state"} |
| tasks: {method: GET, path: /tasks, description: "List all tasks"} |
| grader: {method: POST, path: /grader, description: "Run baseline grader"} |
| health: {method: GET, path: /health, description: "Liveness probe"} |
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| project_structure: |
| - env.py: "Core TrafficEnv class" |
| - tasks.py: "Easy / Medium / Hard configuration dicts" |
| - baseline_agent.py: "Rule-based baseline agent" |
| - inference.py: "FastAPI server + LLM agent + CLI validator script" |
| - test_env.py: "Simulation runner and correctness checks" |
| - openenv.yaml: "This file — environment specification" |
| - README.md: "Full documentation" |
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