gym-graph-traffic

Road traffic simulator for OpenAI Gym

Installation

git clone [email protected]:rltraffic/gym-graph-traffic.git
cd gym-graph-traffic
pip install -e .

Running an example

python3 examples/minimal.py 

Configuration

All parameters considering configuration are available in gym_graph_traffic/envs/params.py file:

Units glossary

According to default values:

• Experiment is a full run of a learning algorithm.

  1 experiment = ??? episodes (set manually by user in training loop)

• Episode is a single complete simulation run from initial state to terminal state. The Agent’s goal it to maximize the total reward it receives during an episode. Different episodes are independent of one another.

  1 episode = 200 steps

• Step is a part of an episode executed under single action. (Number of steps = number of actions taken).

  1 step = 60 seconds (of real traffic simulation)

  1 step = 22 updates (STEP_LENGTH / SECONDS_PER_UPDATE)

• Update is a single step of a Nagel-Schreckenberg cellular automaton model.

  1 update = 2.7 seconds (of real traffic simulation)

Action

Because a single action is a vector representing as an int. We introduced action_int – a single number encoding the action_array. The action_array is a base x representation of action_int, where x is equal to the length of the RED_DURATIONS array.

Example of translation for easy preset:

                 0    1    2    3   # indices
RED_DURATIONS = [0,  20,  40,  60]

The length of the RED_DURATIONS array equals 4, so encoding has a base of 4 (quaternary) and 4 bits of encoding – easy road network has 4 intersections.

      4^3  4^2  4^1  4^0
20   [0,   1,   1,   0]

Above can be seen the quaternary representation of 20 (4^2 + 4^1 = 20). There is a mapping between the bits in the quaternary representation and the RED_DURATIONS array indices. Therefore, the translated action_int = 20 equals [0, 20, 20, 0].

• First and forth intersections will have full, 60-second phase of horizontal flow.
• Second and third intersection will have 20 seconds of vertical, and then 40 seconds of horizontal flow.

Diagram representation of single step.

Reward

Total distance covered by all cars in one step.

Observation

Two vectors – average number of cars and average speed per road segment.

Presets

The model is toroidal (periodic).

By default there are 4 available presets:

• easy - one-lane, one-way road with 4 intersections.
• two_roads - two-way road with 4 intersections.
• grid_4x2 - 4 by 2 grid of two-way roads (with a total of 8 intersections).
• grid_3x3 - 3 by 3 grid of two-way roads (with a total of 9 intersections).

grid_3x3 preset.

Two create different sizes of presets there are two functions supplied in util/grid.py: make_grid and make_line.

Rendering

The environment is not yet compatible with Gym-like rendering. Thus all rendering options have to be supplied within aforementioned params.py file.

Reference table

parameter	default value	description
STEPS_PER_EPISODE	200	Number of steps per episode.
SECONDS_PER_UPDATE	2.7	Number of real traffic simulation seconds per update (In particular, )
STEP_LENGTH	60	Length of step in real traffic simulation seconds.
RED_DURATIONS	[0, 20, 40, 60]	List of all possible actions per intersection.
MAX_SPEED	5	Maximum speed in number of cells that the cars can travel during single update. It corresponds to MAX_SPEED * SECONDS_PER_UPDATE * 3.6 ≈ 50 km/h.
PROB_SLOW_DOWN	0.1	p parameter from Nagel-Schreckenberg model.
PRESET	"grid_3x3"	Current preset.
SEGMENT_LENGTH	100	Length of each segment in number of cells. 1 cell corresponds to 7.5 meter.
CAR_DENSITY	0.125	Probability of a car occupying a cell at the initialization (reset) of simulation. Average number of cars is then equal to NUM_SEGMENTS * SEGMENT_LENGTH * CAR_DENSITY.
RENDER	False	If True then pygame visualisation starts.
RENDER_LIGHT_MODE	False	If True it will allow the light color scheme during render.
RENDER_FPS	30	Maximum frames per second during render.