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README.md

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+# Model Card: DQN Traffic Light Controller (Belgradzka-KEN, Warsaw)
+
+## Model Summary
+This project contains a Deep Q-Network (DQN) model trained to control traffic lights at the **Belgradzka-KEN intersection in Warsaw**.
+
+The model learns signal switching behavior in a SUMO simulation to improve traffic flow metrics such as queue length and delay.
+
+## Intended Use
+- **Primary use**: Research and demonstration of reinforcement learning for traffic signal control in SUMO.
+- **Deployment scope**: This model is trained for **one specific intersection only** (Belgradzka-KEN).
+- **Important limitation**: It is **not** expected to work correctly on other intersections without retraining.
+
+## Training Setup
+- Algorithm: DQN
+- Final selected model:
+	- Training steps: **225,000**
+	- Learning rate: **0.0005**
+- Traffic during training:
+	- Random traffic with fixed car arrival interval
+	- Random traffic with random car arrival interval (closer to real-world variability)
+
+## Demonstration
+GUI recording showing how the trained model controls traffic lights:
+
+![DQN GUI demo](media/demo.gif)
+
+## Training Results
+
+### Effect of Training Steps (Fixed Arrival Interval)
+This plot shows performance trends for models trained with different numbers of steps on random traffic with fixed car arrival interval:
+
+![Fixed interval training steps trend](media/fixed_steps_trend.png)
+
+### Effect of Training Steps (Random Arrival Interval)
+This plot shows performance trends for models trained with different numbers of steps on random traffic with random car arrival interval, intended to better simulate real-world traffic:
+
+![Random interval training steps trend](media/random_steps_trend.png)
+
+### Hyperparameter Tuning
+This plot compares how different hyperparameter settings affect model performance, tested on:
+- low fixed arrival interval
+- medium fixed arrival interval
+- high fixed arrival interval
+- truly random traffic (mix of low/medium/high, closer to real world)
+
+![Hyperparameter tuning results](media/tuning_results.png)
+
+## Final Evaluation and Stability Comparison
+Two models were compared:
+- **Stable/best model**: selected final model
+- **Unstable model**: model with `target_update_interval = 1000` (instead of 500)
+
+The comparison highlights the importance of choosing proper hyperparameters.
+
+### Mean Delay by Method
+![Mean delay comparison](media/mean_delay_by_method.png)
+
+### Mean Queue Length by Method
+![Mean queue length comparison](media/mean_queue_length_by_method.png)
+
+## Usage
+If you want to train, evaluate, or use these models yourself, see the repository:
+
+https://github.com/Tombiczek/rl-traffic-control-sumo

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